文献类型：期刊文献

英文题名：YOLO-ERF: lightweight object detector for UAV aerial images

作者：Wang, Xin[1];He, Ning[1];Hong, Chen[2];Sun, Fengxi[1];Han, Wenjing[1];Wang, Qi[2]

通讯作者：He, N[1]

机构：[1]Beijing Union Univ, Coll Smart City, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China

第一机构：北京联合大学

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Smart City, Beijing 100101, Peoples R China.|[11417]北京联合大学;

年份：2023

外文期刊名：MULTIMEDIA SYSTEMS

收录：;EI(收录号：20233814755620);Scopus(收录号：2-s2.0-85171421881);WOS:【SCI-EXPANDED(收录号:WOS:001069223100003)】；

基金：This work is supported by the National Natural Science Foundation of China (62272049, 62236006, 62172045), the Key Project of Beijing Municipal Commission of Education (KZ201911417048), the Major Project of Technological Innovation 2030 - "New Generation Artiffcial Intelligence" (2018AAA0100800), the Science and Technology Project of Beijing Municipal Commission of Education (KM202111417009, KM201811417005, the Academic Research Projects of Beijing Union University (No.ZKZD202301).

语种：英文

外文关键词：Object detection; UAV; Aerial image; Lightweight; Receptive field

摘要：The application of object detection techniques in the field of unmanned aerial vehicles (UAVs) is an important research direction in computer vision. Because object detection in UAV aerial images needs to meet real-time requirements, a challenging problem in this technology is the trade-off between network parameters and detection accuracy. To solve this problem, this paper proposes a lightweight object detector family named YOLO-ERF. First, this paper proposes the effective receptive field (ERF) module, which can increase the convolutional kernel receptive field while preserving local details. The ERF module is then used to design a lightweight backbone to expand the network receptive field without the need for attaching additional context modules after the backbone to expand the receptive field. In addition, the proposed detectors use the ERF module to critically optimize the path aggregation network structure to improve accuracy with reduced network parameters. Finally, a lightweight detection head is proposed to improve small object recognition in complex backgrounds. With these optimizations, the YOLO-ERF models in this paper achieved a better trade-off between accuracy and parameters than other mainstream models, achieving strong results on the VisDrone and COCO datasets. YOLO-ERF-T reduced the number of network parameters by 40.3% when compared with YOLOv7-Tiny while increasing the average accuracy by 2.4% and 1.9%, respectively, in VisDrone and COCO datasets.