Coal and Gangue Recognition Method Based on Dual-Channel Pseudocolor Image by Lidar

Yan Wang; Jichuan Xing; Yaozhi Wang

doi:10.3788/LOP223222

Journals >Laser & Optoelectronics Progress >Volume 61 >Issue 4 >Page 0437005 > Article

Laser & Optoelectronics Progress
Vol. 61, Issue 4, 0437005 (2024)

Coal and Gangue Recognition Method Based on Dual-Channel Pseudocolor Image by Lidar

Yan Wang, Jichuan Xing^*, and Yaozhi Wang

Author Affiliations

School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China

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DOI: 10.3788/LOP223222 Cite this Article Set citation alerts

Yan Wang, Jichuan Xing, Yaozhi Wang. Coal and Gangue Recognition Method Based on Dual-Channel Pseudocolor Image by Lidar[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0437005 Copy Citation Text

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Example maps of production environment and industrial camera data. (a) Data collection environment; (b) data acquisition by industrial camera

Fig. 1. Example maps of production environment and industrial camera data. (a) Data collection environment; (b) data acquisition by industrial camera

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Fig. 2. Schematic diagram of acquisition system

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Fig. 3. Point cloud images. (a) Distance point cloud; (b) intensity point cloud

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Fig. 4. Image of coal and gangue strength. (a) Coal; (b) gangue

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Fig. 5. Example of distance point cloud

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Fig. 6. Process of image preprocessing

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Fig. 7. Background denoising. (a) Before removal; (b) after removal

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Fig. 8. Point cloud image generation process. (a) Original point cloud map; (b) vertical view of point cloud; (c) real point cloud projection; (d) two dimension intensity pixel image

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Fig. 9. Dual channel pseudo color image generation process. (a) Intensity point cloud; (b) distance point cloud; (c) dual channel pseudo color image

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Fig. 10. DenseNet-121 network structure

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Fig. 11. Dual channel pseudo color image used in the experiment. (a) Coal; (b) gangue

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Fig. 12. Training accuracy and training loss curves. (a) (d) DenseNet-40; (b) (e) DenseNet-121; (c) (f) DenseNet-201

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Parameter	Value
Scanning frequency /Hz	600
Laser wavelength /nm	660
Scanning range /（°）	70
Angular resolution /（°）	0.0833
Working distance /m	0.7‒3
Measurement accuracy /mm	±1

Table 1. Parameters of LMS4000

Layer	Output size	DenseNet-40	DenseNet-121
Convolution	112×112	7×7Conv，s=2	7×7Conv，s=2
Pooling	56×56	3×3max pool，s=2	3×3max pool，s=2
Dense block（1）	56×56	$[\binom{1 \times 1 C o n v}{3 \times 3 C o n v}] \times 6$	$[\binom{1 \times 1 C o n v}{3 \times 3 C o n v}] \times 6$
Transition layer（1）	56×56	1×1Conv	1×1Conv
Transition layer（1）	28×28	2×2average pool，s=2	2×2average pool，s=2
Dense block（2）	28×28	$[\binom{1 \times 1 C o n v}{3 \times 3 C o n v}] \times 6$	$[\binom{1 \times 1 C o n v}{3 \times 3 C o n v}] \times 12$
Transition layer（2）	28×28	1×1Conv	1×1Conv
Transition layer（2）	14×14	2×2average pool，s=2	2×2average pool，s=2
Dense block（3）	14×14	$[\binom{1 \times 1 C o n v}{3 \times 3 C o n v}] \times 6$	$[\binom{1 \times 1 C o n v}{3 \times 3 C o n v}] \times 24$
Transition layer（3）	14×14		1×1Conv
Transition layer（3）	7×7		2×2average pool，s=2
Dense block（4）	7×7		$[\binom{1 \times 1 C o n v}{3 \times 3 C o n v}] \times 16$
Classification layer	1×1	7×7global average pool	7×7global average pool
Classification layer		1000D fully-connecter，softmax	1000D fully-connecter，softmax

Table 2. Parameters of DenseNet-40 and DenseN-121

Model	Size /10⁶	FLOPs /10⁹
AlexNet	61.0	0.7
GoogleNet	7.0	1.6
VGG-19	144.0	15.5
ResNet50	25.0	3.9
DenseNet40	0.2	1.3
DenseNet121	8.0	5.7

Table 3. FLOPs and parameter quantity of common models

Model	Accuracy /%	P	R	F₁
DenseNet-40	94.56	0.96	0.97	0.97
DenseNet-121	93.49	0.91	0.97	0.94
DenseNet-201	92.74	0.90	0.96	0.93
ResNet-101	83.53	0.82	0.86	0.84
VGG-19	92.54	0.90	0.95	0.93
Xception	90.24	0.89	0.92	0.90

Table 4. Cross validation average results

Yan Wang, Jichuan Xing, Yaozhi Wang. Coal and Gangue Recognition Method Based on Dual-Channel Pseudocolor Image by Lidar[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0437005

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