Efficient Residual Bottleneck for Object Detection on CPU

Object detection is the most fundamental and important task in computer vision. With the development of hardware such as computing power of GPUs and cameras, object detection technology is gradually improving. However, there are many difficulties in using GPUs in industrial fields. Therefore, it is very important to use efficient deep learning technology in the CPU environment. In this paper, we propose a deep learning model that can detect objects in real-time from images and videos using CPU. By modifying the CSP [1] bottleneck, which corresponds to the backbone of YOLOv5 [2], an experiment was conducted to reduce the amount of computation and improve the FPS. The model was trained using the MS COCO dataset, and compared with the original YOLOv5, the number of parameters was reduced by about 2.4%, and compared with RefineDetLite, the mAP value was measured to be 0.367 mAP, which is 0.071 higher than that of RefineDetLite. The FPS was 23.010, which was sufficient for real-time object detection.