Due to airborne infrared detection device under the limit of detection range will cause the target to be detected in the image of pixels smaller, less radiation, easy to drown in the background. Meanwhile high speed and dim target in complex scene change, however, traditional algorithm only by artificial convolution kernel parameters for object detection and segmentation threshold, will cause more false alarm. To solve this problem, a lightweight dim target detection method based on CNN neural network architecture is proposed in this paper, which effectively improves the detection rate of dim target and reduces the false alarm rate. Through simulation comparison and statistics, it is verified that the detection rate of this algorithm can reach 93%~98% in different scenes, and the average number of false alarms is 0.1~2.6 in a single frame, which realizes the low false alarm detection of targets.
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