【摘要】 针对Yolov3算法应用于人体检测中的准确率低,参数量、计算量和模型体积大难以在资源有限的嵌入式平台上实现等问题,提出了YOLOv3改进及其模型压缩算法。在YOLOv3中通过引入密集连接与多分支结构,增加网络宽度和多尺度感受野,加强特征重用,提高了模型的检测精度;对改进的YOLOv3通过联合优化权重损失函数和BN层缩放因子的L₁正则项等方式进行通道剪枝,从而减小了参数量和计算量,模型体积得到了大幅压缩。实验结果表明,改进后YOLOv3算法的检测精度提升了6.01%,模型体积减小了38.46%;经过压缩后,模型的检测精度虽然降低了3.16%,但模型体积仅为原来的3.31%,只有4.77 MB。因此,通过改进和压缩后的YOLOv3仍然保持较高的检测精度,而且模型体积得到大幅度的压缩,为YOLOv3模型在嵌入式平台上实现人体检测提供了支撑。更多还原

【Abstract】 Aiming at the problems of low accuracy, large amount of parameters, large amount of calculation, and large model volume, difficulty of being implemented on embedded platforms with limited resources when YOLOv3 algorithm is applied to human body detection, an improved YOLOv3 and its model compression algorithm are proposed. By introducing dense connections and multi-branch structure in YOLOv3, increasing the network width and multi-scale receptive field, and strengthening feature reuse, the accuracy of the model is improved. For the improved YOLOv3, channel pruning is performed by jointly optimizing the weight loss function and the L₁ regular term of the BN layer scaling factor, thereby reducing the amount of parameters and calculations, and the volume of the model is greatly compressed. Experimental results show that the accuracy of the improved YOLOv3 algorithm is increased by 6.01%, and the model volume is reduced by 38.46%. After compression, although the accuracy of the model is reduced by 3.16%, the model volume is only 3.31% of the original, only 4.77 MB. Therefore, the improved and compressed YOLOv3 still maintains a high accuracy rate, and the model volume is greatly reduced, which provides support for the YOLOv3 model to realize human body detection in embedded deployment.更多还原