The target detection accuracy in the imaging test phase of armored vehicle dynamic performance assessment is closely related to the precision of weapon equipment identification and qualification.To address the degradation issues such as low target image contrast and poor discernibility,a degraded target detection algorithm based on improved YOLOv5 is proposed.The proposed algorithm utilizes a multi-branch grouping convolutional structure combined with deep and pointwise convolutions to construct a backbone feature extraction network,thus reducing the computational complexity of network parameters and improving the detection speed.The representation attention mechanism is introduced to enhance the representation capability of the targets.At the network output layer,a three-branch spatial feature fusion is introduced to combine the fine-grained feature information from low-level feature maps and the rich semantic information from high-level feature maps,preserving the details and edge semantic information of degraded target images.Experimental results demonstrate that,in the target dataset,the proposed algorithm achieves a detection accuracy of 90.88%in terms of mean average precision(mAP)and a detection speed of 52.74 fps.It can efficiently and accurately complete the target detection phase in the imaging test of dynamic performance assessment.
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