Electric Tricycle Detection Based on Improved YOLOv5s Model
Xiaofang Ou, Fengchun Han, Jing Tian, Jijie Tang, and Zhengtao Yang
To address the problems related to target detection of electric tricycles in road traffic management in China and the shortcomings of current detection models in small target detection and real-time performance, this study proposes a detection method based on an improved YOLOv5s model. The original YOLOv5s model is first improved by adding a small object detection head and by introducing a Transformer structure that combines an efficient additive attention mechanism, and then a dataset based on urban road scenes is built. The model is improved in terms of accuracy, recall, and mean average precision (mAP@0.5) by 0.67%, 2.68%, and 5.78%, respectively. The model also achieves a frame rate of 92 frame/s and demonstrates good processing capabilities, thus meeting the real-time detection requirements for actual road traffic situations.To address the problems related to target detection of electric tricycles in road traffic management in China and the shortcomings of current detection models in small target detection and real-time performance, this study proposes a detection method based on an improved YOLOv5s model. The original YOLOv5s model is first improved by adding a small object detection head and by introducing a Transformer structure that combines an efficient additive attention mechanism, and then a dataset based on urban road scenes is built. The model is improved in terms of accuracy, recall, and mean average precision (mAP@0.5) by 0.67%, 2.68%, and 5.78%, respectively. The model also achieves a frame rate of 92 frame/s and demonstrates good processing capabilities, thus meeting the real-time detection requirements for actual road traffic situations.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0215005 (2025)
- DOI：10.3788/LOP241065
Fine Classification of Tree Species Based on Improved U-Net Network
Yulin Cai, Hongzhen Gao, Xiaole Fan, Huiyu Xu, Zhengjun Liu, and Geng Zhang
In this study, a new method is proposed by improving an existing deep-learning network, where aerial high-resolution hyperspectral data and LiDAR data are combined for the fine classification of tree species. First, feature extraction and fusion are performed for different data sources. Subsequently, a classification network named CA-U-Net is constructed based on the U-Net network by adding a channel-attention-mechanism module to adjust the weights of different features adaptively. Finally, we attempt to address the problem of low identification precision for small-sample species by modifying CA-U-Net in class-imbalance cases. The research results show that 1) the CA-U-Net network performs well, with an overall classification accuracy of 96.80%. Compared with the FCN, SegNet, and U-Net networks, the CA-U-Net network shows improvements of 8.56, 11.99, and 3.31 percent points, respectively, in terms of classification accuracy. Additionally, the network exhibits a higher convergence speed. 2) Replacing the original loss function in the CA-U-Net network with a cross-entropy loss function based on the class-sample-size balance can improve the classification accuracy for tree species with fewer samples. The proposed methodology can serve as an important reference in small-scale forestry, such as orchard management, urban-forest surveys, and forest-diversity surveys.In this study, a new method is proposed by improving an existing deep-learning network, where aerial high-resolution hyperspectral data and LiDAR data are combined for the fine classification of tree species. First, feature extraction and fusion are performed for different data sources. Subsequently, a classification network named CA-U-Net is constructed based on the U-Net network by adding a channel-attention-mechanism module to adjust the weights of different features adaptively. Finally, we attempt to address the problem of low identification precision for small-sample species by modifying CA-U-Net in class-imbalance cases. The research results show that 1) the CA-U-Net network performs well, with an overall classification accuracy of 96.80%. Compared with the FCN, SegNet, and U-Net networks, the CA-U-Net network shows improvements of 8.56, 11.99, and 3.31 percent points, respectively, in terms of classification accuracy. Additionally, the network exhibits a higher convergence speed. 2) Replacing the original loss function in the CA-U-Net network with a cross-entropy loss function based on the class-sample-size balance can improve the classification accuracy for tree species with fewer samples. The proposed methodology can serve as an important reference in small-scale forestry, such as orchard management, urban-forest surveys, and forest-diversity surveys.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0228002 (2025)
- DOI：10.3788/LOP241175
Reliable Polyp Segmentation Based on Local Channel Attention
Jian Xu, and Ruohan Wang
To enhance the accuracy and credibility of existing polyp segmentation methods, this study proposes a reliable segmentation technique that uses local channel attention. First, the improved pyramid vision transformer is employed to extract polyp region features, thereby addressing the insufficient feature extraction capabilities of traditional convolutional neural networks. In addition, a local channel attention mechanism is applied to fuse cascade features, and the edge detail information is gradually recovered to enhance the overall representational capability of the model while ensuring accurate polyp localization. Finally, a trusted polyp segmentation model is developed based on subjective logic evidence to derive the probability and uncertainty of the polyp segmentation problem, and a plausibility measure is applied to the segmentation results. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art polyp segmentation techniques in terms of accuracy, robustness, and generalization, leading to more reliable polyp segmentation results.To enhance the accuracy and credibility of existing polyp segmentation methods, this study proposes a reliable segmentation technique that uses local channel attention. First, the improved pyramid vision transformer is employed to extract polyp region features, thereby addressing the insufficient feature extraction capabilities of traditional convolutional neural networks. In addition, a local channel attention mechanism is applied to fuse cascade features, and the edge detail information is gradually recovered to enhance the overall representational capability of the model while ensuring accurate polyp localization. Finally, a trusted polyp segmentation model is developed based on subjective logic evidence to derive the probability and uncertainty of the polyp segmentation problem, and a plausibility measure is applied to the segmentation results. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art polyp segmentation techniques in terms of accuracy, robustness, and generalization, leading to more reliable polyp segmentation results.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0217002 (2025)
- DOI：10.3788/LOP241160
Advances in Optical Coherence Elastography and Its Applications
Yirui Zhu, Jiulin Shi, Lingkai Huang, Lihua Fang, Tomas E. Gomez Alvarez-Arenas, and Xingdao He
Since its introduction in 1998, optical coherent elastography technology has significantly advanced in detecting and imaging of the biomechanical properties of soft tissues over the past two decades. This technology stands out owing to its high spatial resolution, sensitivity in measuring elastic moduli, and rapid imaging speed, making it one of the most promising optical elastography technologies for clinical application. At present, research groups worldwide are focusing on three main core elements of optical coherent elastography technology: developing safer and more effective excitation methods to generate the necessary vibration signals for elasticity evaluation, establishing new mechanical models to accurately quantify the biomechanical properties of tissues under complex boundary conditions, and developing new algorithms for the quantitative analysis of biomechanical properties. These efforts aim to accelerate the clinical application and transformation of this technology. This article reviews the fundamental theories and latest advancements in optical coherent elastography, explores noncontact approaches, establishes mechanical wave models for various biological tissues, and outlines future directions to facilitate its clinical application.Since its introduction in 1998, optical coherent elastography technology has significantly advanced in detecting and imaging of the biomechanical properties of soft tissues over the past two decades. This technology stands out owing to its high spatial resolution, sensitivity in measuring elastic moduli, and rapid imaging speed, making it one of the most promising optical elastography technologies for clinical application. At present, research groups worldwide are focusing on three main core elements of optical coherent elastography technology: developing safer and more effective excitation methods to generate the necessary vibration signals for elasticity evaluation, establishing new mechanical models to accurately quantify the biomechanical properties of tissues under complex boundary conditions, and developing new algorithms for the quantitative analysis of biomechanical properties. These efforts aim to accelerate the clinical application and transformation of this technology. This article reviews the fundamental theories and latest advancements in optical coherent elastography, explores noncontact approaches, establishes mechanical wave models for various biological tissues, and outlines future directions to facilitate its clinical application.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0200002 (2025)
- DOI：10.3788/LOP241618
Turbulence-Blurred Target Restoration Algorithm with a Nonconvex Regularization Constraint
Xinggui Xu, Hong Li, Bing Ran, Weihe Ren, and Junrong Song
A turbulent fuzzy target restoration algorithm with a nonconvex regularization constraint is proposed to address degradation issues, such as low signal-to-noise ratio, blurring, and geometric distortion, in target images caused by atmospheric turbulence and light scattering in long-range optoelectronic detection systems. First, we utilized latent low-rank spatial decomposition (LatLRSD) to obtain the target low-rank components, texture components, and high-frequency noise components. Next, two structural components were obtained by denoising the LatLRSD model; these were weighted and reconstructed in the wavelet transform domain, and nonconvex regularization constraints were added to the constructed target reconstruction function to improve the reconstruction blur and scale sensitivity problems caused by the traditional lp norm (p=0,1,2) as a constraint term. The results of a target restoration experiment in long-distance turbulent imaging scenes show that compared with traditional algorithms, the proposed algorithm can effectively remove turbulent target blur and noise; the average signal-to-noise ratio of the restored target is improved by about 9 dB. Further, the proposed algorithm is suitable for multiframe or single-frame turbulent blur target restoration scenes.A turbulent fuzzy target restoration algorithm with a nonconvex regularization constraint is proposed to address degradation issues, such as low signal-to-noise ratio, blurring, and geometric distortion, in target images caused by atmospheric turbulence and light scattering in long-range optoelectronic detection systems. First, we utilized latent low-rank spatial decomposition (LatLRSD) to obtain the target low-rank components, texture components, and high-frequency noise components. Next, two structural components were obtained by denoising the LatLRSD model; these were weighted and reconstructed in the wavelet transform domain, and nonconvex regularization constraints were added to the constructed target reconstruction function to improve the reconstruction blur and scale sensitivity problems caused by the traditional lp norm (p=0,1,2) as a constraint term. The results of a target restoration experiment in long-distance turbulent imaging scenes show that compared with traditional algorithms, the proposed algorithm can effectively remove turbulent target blur and noise; the average signal-to-noise ratio of the restored target is improved by about 9 dB. Further, the proposed algorithm is suitable for multiframe or single-frame turbulent blur target restoration scenes.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0237001 (2025)
- DOI：10.3788/LOP240707
Roadside Object Detection Algorithm Based on Multiscale Sequence Fusion
Ruoying Liu, Miaohua Huang, Liangzi Wang, Yongkang Hu, and Ye Tao
This study develops a lightweight roadside object detection algorithm called MQ-YOLO. The algorithm is based on multiscale sequence fusion. It addresses the challenges of low detection accuracy for small and occluded targets and the large number of model parameters in urban traffic roadside object detection tasks. We design a D-C2f module based on multi-branch feature extraction to enhance feature representation while maintaining speed. To strengthen the integration of information from multiscale sequences and enhance feature extraction for small targets, the plural-scale sequence fusion (PSF) module is designed to reconstruct the feature fusion layer. Multiple attention mechanisms are incorporated into the detection head for greater focus on the salient semantic information of occluded targets. To enhance the detection performance of the model, a loss function based on the normalized Wasserstein distance is introduced. Experimental results on the DAIR-V2X-I dataset demonstrate that MQ-YOLO achieves improved mAP@50 and mAP@(50?95) by 3.9 percentage point and 6.0 percentage point compared to the valuses obtained with baseline YOLOv8n with 3.96 Mbit parameters. Experiments on the DAIR-V2X-SPD-I dataset show that the model has good generalizability. During roadside deployment, the model reaches detection speeds of 62.5 frame/s, meeting current roadside object detection requirement for edge deployment in urban traffic.This study develops a lightweight roadside object detection algorithm called MQ-YOLO. The algorithm is based on multiscale sequence fusion. It addresses the challenges of low detection accuracy for small and occluded targets and the large number of model parameters in urban traffic roadside object detection tasks. We design a D-C2f module based on multi-branch feature extraction to enhance feature representation while maintaining speed. To strengthen the integration of information from multiscale sequences and enhance feature extraction for small targets, the plural-scale sequence fusion (PSF) module is designed to reconstruct the feature fusion layer. Multiple attention mechanisms are incorporated into the detection head for greater focus on the salient semantic information of occluded targets. To enhance the detection performance of the model, a loss function based on the normalized Wasserstein distance is introduced. Experimental results on the DAIR-V2X-I dataset demonstrate that MQ-YOLO achieves improved mAP@50 and mAP@(50?95) by 3.9 percentage point and 6.0 percentage point compared to the valuses obtained with baseline YOLOv8n with 3.96 Mbit parameters. Experiments on the DAIR-V2X-SPD-I dataset show that the model has good generalizability. During roadside deployment, the model reaches detection speeds of 62.5 frame/s, meeting current roadside object detection requirement for edge deployment in urban traffic.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0237005 (2025)
- DOI：10.3788/LOP241187
Design of a Miniaturized Cell Microscopic Image Acquisition System Based on Wireless Transmission
Runna Liu, Jing Liu, Hua Tian, Santing Zheng, and Tingting Zheng
A miniaturized, low-cost, and portable cell microscopic image acquisition system designed for wireless transmission has been developed, using Unigraphics NX software and three-dimensional printing for system structure to achieve cost efficiency and lightweight. The system measures 13 cm×5 cm×20 cm and weighs ~1.5 kg. An intelligent microimage acquisition software platform is built using functions from the OpenCV library. Testing results demonstrate that the proposed system successfully transmits microscopic images wirelessly via WiFi, performs cell counting and cell migration, and meets the requirements of standard cell observation experiments. The resolution of system is 1.5 μm, which is suitable for both teaching and scientific research applications.A miniaturized, low-cost, and portable cell microscopic image acquisition system designed for wireless transmission has been developed, using Unigraphics NX software and three-dimensional printing for system structure to achieve cost efficiency and lightweight. The system measures 13 cm×5 cm×20 cm and weighs ~1.5 kg. An intelligent microimage acquisition software platform is built using functions from the OpenCV library. Testing results demonstrate that the proposed system successfully transmits microscopic images wirelessly via WiFi, performs cell counting and cell migration, and meets the requirements of standard cell observation experiments. The resolution of system is 1.5 μm, which is suitable for both teaching and scientific research applications.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0211005 (2025)
- DOI：10.3788/LOP241029
Region Growth Method Based on Boundary Characteristics for an Improved Poisson Surface Reconstruction Algorithm
Xiaoqi Ma, Wenhua Ye, Chaohong Zhang, Weifang Chen, and Jie Fu
The traditional Poisson surface reconstruction algorithm often generates pseudo-closed surfaces along the internal and external edges, resulting in a deviation of the reconstruction results. To address this issue, an improved method based on boundary region growth is proposed. First, based on the Poisson surface, the object boundary points are extracted by normal lines and the continuous boundary curves are fitted. Then, vector cross multiplication is utilized to screen the neighboring points of the boundary and extract the boundary features. Finally, a double-ray method is employed to extract initial seed points for the boundary inner surface, and the Poisson surface is segmented using the region growth method, which is constrained by the boundary characteristics. Experimental results show that the proposed method can effectively eliminate pseudo-closed surfaces in the model and enhance the accuracy and integrity of reconstructed surfaces. This method has good adaptability and performance stability with different types of objects.The traditional Poisson surface reconstruction algorithm often generates pseudo-closed surfaces along the internal and external edges, resulting in a deviation of the reconstruction results. To address this issue, an improved method based on boundary region growth is proposed. First, based on the Poisson surface, the object boundary points are extracted by normal lines and the continuous boundary curves are fitted. Then, vector cross multiplication is utilized to screen the neighboring points of the boundary and extract the boundary features. Finally, a double-ray method is employed to extract initial seed points for the boundary inner surface, and the Poisson surface is segmented using the region growth method, which is constrained by the boundary characteristics. Experimental results show that the proposed method can effectively eliminate pseudo-closed surfaces in the model and enhance the accuracy and integrity of reconstructed surfaces. This method has good adaptability and performance stability with different types of objects.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0211001 (2025)
- DOI：10.3788/LOP240984
Adaptive Stereo Matching Based on Local Information Entropy and Improved AD-Census Transform
Jingfa Lei, Zihan Wei, Yongling Li, Ruhai Zhao, and Miao Zhang
To enhance the accuracy of traditional local stereo matching algorithms in weak-texture regions and address the limitations of the AD-Census transform in adapting to local region features during cost fusion, an adaptive stereo matching algorithm based on local information entropy and an improved AD-Census transform is proposed. In the cost calculation stage, the local information entropy of the input image is first computed. Then, based on the entropy of the pixel neighborhood, an adaptive window size is selected to refine the Census transform. Next, an adaptive fusion weight is determined from the local information entropy to combine the improved Census and AD costs. In the cost aggregation stage, a unidirectional dynamic programming aggregation algorithm is introduced. After disparity computation and optimization, the final disparity map is produced. The algorithm is evaluated on the Middlebury platform using standard test images. Experimental results indicate that the proposed algorithm achieves an average mismatch rate of 5.94% in non-occluded areas and 8.37% across all areas, outperforming many existing algorithms in terms of matching quality and robustness to noise.To enhance the accuracy of traditional local stereo matching algorithms in weak-texture regions and address the limitations of the AD-Census transform in adapting to local region features during cost fusion, an adaptive stereo matching algorithm based on local information entropy and an improved AD-Census transform is proposed. In the cost calculation stage, the local information entropy of the input image is first computed. Then, based on the entropy of the pixel neighborhood, an adaptive window size is selected to refine the Census transform. Next, an adaptive fusion weight is determined from the local information entropy to combine the improved Census and AD costs. In the cost aggregation stage, a unidirectional dynamic programming aggregation algorithm is introduced. After disparity computation and optimization, the final disparity map is produced. The algorithm is evaluated on the Middlebury platform using standard test images. Experimental results indicate that the proposed algorithm achieves an average mismatch rate of 5.94% in non-occluded areas and 8.37% across all areas, outperforming many existing algorithms in terms of matching quality and robustness to noise.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0215004 (2025)
- DOI：10.3788/LOP240968
A Novel Three-Dimensional Point Cloud Matching Algorithm Based on Point Region Features and Weighted Voting
Junjun Lu, Ke Ding, Zuoxi Zhao, and Feng Wang
The traditional point pair features (PPF) algorithm lacks sufficient point cloud matching accuracy in precision industrial production and robustness to planar point clouds. To address these issues, this study proposes a novel point regions features (PRF) registration method. In this method, PRF point domain features enhance matching by incorporating the feature complexity and average direction of target point pairs within their respective neighborhoods as complementary features. The algorithm utilizes the complexity of different point domains as a weighted criterion for feature matching, conducting a weighted voting process. The point cloud is then obtained in the real working scene. Experimental results from common point cloud matching experiments in real-world scenarios show that the proposed PRF registration algorithm significantly improves point cloud accuracy and robustness with minimal impact on speed.The traditional point pair features (PPF) algorithm lacks sufficient point cloud matching accuracy in precision industrial production and robustness to planar point clouds. To address these issues, this study proposes a novel point regions features (PRF) registration method. In this method, PRF point domain features enhance matching by incorporating the feature complexity and average direction of target point pairs within their respective neighborhoods as complementary features. The algorithm utilizes the complexity of different point domains as a weighted criterion for feature matching, conducting a weighted voting process. The point cloud is then obtained in the real working scene. Experimental results from common point cloud matching experiments in real-world scenarios show that the proposed PRF registration algorithm significantly improves point cloud accuracy and robustness with minimal impact on speed.
- Jan. 25, 2025
- Laser & Optoelectronics Progress
- Vol. 62, Issue 2, 0215007 (2025)
- DOI：10.3788/LOP241055
AI-enabled universal image-spectrum fusion spectroscopy based on self-supervised plasma modeling
Advanced Photonics Nexus, Vol. 3,Issue 6, 066014 (2024)
Progress of Type I Organic Photosensitizers for Photodynamic Therapy
Chinese Journal of Lasers, Vol. 51,Issue 15, 1507201 (2024)
Chinese Journal of Lasers, Vol. 51,Issue 15, 1507402 (2024)
Chinese Journal of Lasers, Vol. 51,Issue 1, 0121001 (2024)
Limit of Laser Protection Capability of Arcsine Coded Imaging System
Acta Optica Sinica, Vol. 44,Issue 10, 1026026 (2024)
Stimulation and imaging of neural cells via photonic nanojets
Photonics Research, Vol. 12,Issue 8, 1604 (2024)
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