A single-frame infrared dim and small target detection algorithm is proposed for the remote infrared target detection system based on the mobile platform. The system faces challenges in detecting targets due to the platform's movement and changes in the background, leading to false alarms. To address this problem, the proposed algorithm combines guided filtering and nine-square-grid filtering for target enhancement, performing block adaptive threshold segmentation through regions of different complexity to maintain a low false alarm rate while detecting targets in different complex scenes. The background of the image is estimated using guided filtering with edge-preserving characteristics to alleviate edge clutter interference. The local grayscale maximum characteristic of dim and small targets is used to calculate the probability of the target using a nine-square filter based on soft threshold non-maximum suppression. Areas that don't satisfy the target characteristics in the background suppression results are eliminated by weighting. A block adaptive threshold segmentation method is proposed to extract the candidate target using the sigmoid function to design the mapping curve of the standard deviation of gray value to parameter k for threshold calculation. The proposed method outperforms classical methods such as Top-Hat, LCM, and Max-Median, with Signal-to-noise Ratio (SNR) and Background Suppression Factor (BSF) indicators maintained at optimal and sub-optimal levels. The recall rates of scenes with different complexity under constant false alarm respectively reached 87.97%, 84.93%, and 86.22%, improving the recall rate of infrared dim and small target detection. The algorithm is adaptable in engineering applications, as demonstrated by the addition of multi-frame target association on the basis of single-frame image detection. The hardware transplant of the infrared dim and small target detection algorithm is implemented using FPGA+DSP signal processing architecture, achieving a processing speed of over 75 frame/s for object detection on 320×256 infrared images. Therefore, the proposed algorithm effectively realizes the real-time detection of dim and small targets with low false alarm rates and scene robustness.