ERF-YOLO: A YOLO algorithm compatible with fewer parameters and higher accuracy

Research shows that theoretical receptive field and effective receptive field are very important to target detection results. The effective receptive field determines the contribution of different positions in the theoretical receptive field. Therefore, the main purpose of this work is to increase the effective receptive field area and reduce the number of parameters. This idea obtains a high-precision and high-speed target detector. First, the algorithm needs to optimize the activation function to improve the efficiency of feature extraction. Second, the model structure needs to select the backbone network and improve the convolutional layer structure. Then, the enhanced network requires increasing the number of the residual structures and the Concat to improve feature extraction performance. Finally, the network needs to combine the optimized convolutional layer and the anchor box loss function to improve the performance of the anchor box. The project designed a YOLO algorithm (ERF-YOLO) with a larger effective receptive field. The training and testing of the experiment use PASCAL VOC data set and MS COCO data set respectively. Experimental results show that the parameter of ERF-YOLO is close to half of YOLO v4. In terms of detection accuracy, ERF-YOLO is superior to many current algorithms. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study focuses on improving the performance of target detection by increasing the effective receptive field area and reducing the number of parameters, ultimately designing the ERF-YOLO algorithm. Experimental results demonstrate that ERF-YOLO outperforms many current algorithms in detection accuracy, showing good performance.