【摘要】 针对装甲目标图像背景复杂、目标尺度小等问题，提出一种基于YOLOv5s的装甲目标检测算法。首先在FPN结构中增加一个浅层分支，增强对小目标特征的提取能力；其次通过Focal Loss损失函数来平衡正负样本；再次将CIoU＿loss用作边框回归损失函数，用以提升识别精度；最后将ECA注意力模块引入算法中，加强重要特征的表达。实验结果表明，改进算法在自制数据集上AP达到92.9%，相较于原始算法提高了4.2%，能够很好地满足装甲目标检测任务的精度与速度需求。更多还原

【Abstract】 Aiming at the problems of complex background and small target scale of armored target image, an armored target detection algorithm based on YOLOv5s is proposed. First, a shallow branch is added to the FPN structure to enhance the ability of extracting small target features; Secondly, the Focal Loss loss function is used to balance the positive and negative samples; CIoU＿ Loss is used as the loss function of frame regression to improve the recognition accuracy; Finally, ECA attention module is introduced into the algorithm to enhance the expression of important features. The experimental results show that AP of the improved algorithm on the self-made data set achieves 92.9%, which is 4.2% higher than that of the original algorithm, and can well meet the accuracy and speed requirements of the armored target detection task.更多还原