In view of the shortcomings of YOLO object detection algorithm in small object detection, and the difficulty of achieving real-time performance on embedded platforms, this paper designs an improved YOLO object detection algorithm, called dense_YOLO. The algorithm contains two phases: feature extraction phase and object detection regression phase. In the feature extraction phase, based on the idea of DenseNet structure, a new slim-densenet feature extraction module based on deep separable convolution is designed, which enhances the transmission of small object features and reduces the parameter quantity to accelerate the network propagation speed. In the object detection stage, the idea of adaptive multi-scale fusion detection is proposed to fuse the extracted features, and the objects are classified and regressed on different feature scales, which improves the detection accuracy of small objects. Experimental results show that, compared with the original YOLO object detection algorithm, the dense_YOLO object detection algorithm improves mAP by 7%, decreases the single picture detection time by 15 ms, and reduces the model size by 90 MB.
