• 人工智能与仿真 •

### 基于改进YOLOv4的轻量化目标检测算法

1. 湖北大学 计算机与信息工程学院，武汉 430062
• 收稿日期:2021-05-10 修回日期:2021-09-22 发布日期:2021-09-22 出版日期:2021-09-24
• 通讯作者: 夏一帆

### Lightweight object detection algorithm based on improved YOLOv4

• Received:2021-05-10 Revised:2021-09-22 Online:2021-09-22 Published:2021-09-24

Abstract: YOLOv4(You Only Look Once version 4) object detection network has complex structure, many parameters, high configuration required for training and low FPS(Frames Per Second) for real-time detection.In order to solve the above problems, a lightweight object detection algorithm ,ML-YOLO(Mobilenetv3Lite-You Only Look Once), based on YOLOv4 was proposed. Firstly, Mobilenetv3 was used to replace the backbone feature extraction network of YOLOv4 , which greatly reduced the amount of backbone network parameters through the deep separable convolution in Mobilenetv3; Then, a simplified Bi-FPN(weighted Bi-directional Feature Pyramid Network) structure was used to replace the feature fusion network of YOLOv4 .The object detection accuracy was optimized by the attention mechanism in Bi-FPN; Finally, the final prediction frame was generated through the YOLOv4 decoding algorithm to achieve the detection of the objects . The experimental results on the VOC(Visual Object Classes)2007 data set show that, the mAP(mean Average Precision) of the ML-YOLO algorithm reaches 80.22%, which is 3 percentage points lower than that of the YOLOv4 algorithm, and 3 percentage points higher than that of the YOLOv5m algorithm; In addition, the model size is only 44.75M ,compared with the YOLOv4 algorithm, it is reduced by 199.5M, and compared with the YOLOv5m algorithm, it is only 2.8M higher. The results prove that the proposed ML-YOLO model, on the one hand, greatly reduces the size of the model compared with the YOLOv4 model, on the other hand, it maintains a higher detection accuracy, indicating that the algorithm can meet the requirements of lightweight and accuracy for mobile or embedded devices.