Great progresses have been achieved on object recognition in the wild. However, it still remains a challenging problem due to tiny objects. Likewise, post-disaster or post-war personnel search and rescue missions often encounter specific problems of complex and wide areas and weak human targets. This work proposes a weak human target recognition technique for unmanned personnel search and rescue. Firstly, referring to human visual cognitive mechanism based on visible light detection equipment, deep convolutional neural network is applied to enable the model to perceive the targets in complex background. Then a single-shot detector is designed to extract the scale adaptive depth feature to match the regions of interest. Last, a deep network model compression algorithm and an efficient online processing framework are designed to speed up and detect the targets.
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