YOLOv4 （You Only Look Once version 4） object detection network has complex structure， many parameters， high configuration required for training and low Frames Per Second （FPS） for real-time detection. In order to solve the above problems， a lightweight object detection algorithm based on YOLOv4， named ML-YOLO （MobileNetv3Lite-YOLO）， 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 depthwise separable convolution in MobileNetv3. Then， a simplified weighted Bi-directional Feature Pyramid Network （Bi-FPN） structure was used to replace the feature fusion network of YOLOv4. Therefore， the object detection accuracy was optimized by the attention mechanism in Bi-FPN. Finally， the final prediction box was generated through the YOLOv4 decoding algorithm， and the object detection was realized. Experimental results on VOC （Visual Object Classes） 2007 dataset show that the mean Average Precision （mAP） of the ML-YOLO algorithm reaches 80.22%， which is 3.42 percentage points lower than that of the YOLOv4 algorithm， and 2.82 percentage points higher than that of the YOLOv5m algorithm； at the same time， the model size of the ML-YOLO algorithm is only 44.75 MB， compared with the YOLOv4 algorithm， it is reduced by 199.54 MB， and compared with the YOLOv5m algorithm， it is only 2.85 MB larger. Experimental results prove that the proposed ML-YOLO model greatly reduces the size of the model compared with the YOLOv4 model while maintaining a higher detection accuracy， indicating that the proposed algorithm can meet the lightweight and accuracy requirements of mobile or embedded devices for object detection.