In order to further reduce the occupation of computing resources by the clothing object detection model based on deep learning, an improved lightweight clothing object detection method, MV3L-YOLOv5, was proposed. The MobileNetV3_Large is used to construct the backbone network of YOLOv5, and the label smoothing strategy was introduced to enhance the generalization ability at the training stage of the model. The data augmentation technology was used to make up for the unbalanced number of images of different clothing categories in the DeepFashion2 dataset. Experimental results show that the model volume of MV3L-YOLOv5 is 10. 27 MB, the floating-point operations is 10. 2×109 times, and mean average precision is 76. 6 %. Comparing with YOLOv5s, which is the lightest network in YOLOv5 series, MV3L-YOLOv5 is compressed in the model volume by 26. 4 %, reduced the floating-point operations by 39 %, and improved accuracy by 1. 3 %. Experimental results in the improved algorithm show that the detection performance is notably improved, and the model is lighter and more suitable for deployment in devices with limited resources.
关键词
深度学习/目标检测/服装图像/轻量级网络/YOLOv5
Key words
deep learning/object detection/clothing image/lightweight network/YOLOv5
万方数据