AN MULTI-SCALE OBJECT DETECTION ALGORITHM COMBINING CONTEXT INFORMATION AND ADAPTIVE RECEPTIVE FIELD

Object tection faces many challenges in the practical application of various complex scenes, such as object occlusion, illumination changes, and object size changes in the practical application. In order to improve the performance of multi-scale target detection, this paper proposes an improved feature pyramid network (FPN) target detection algorithm. Based on the FPN framework, the context information fusion was introduced to utilize the relevance of an object to its surrounding environment and enhance feature representation of objects for wide dynamic range images and to improve the ability of detection ability for different scales. In addition, a cross-channel attention mechanism was constructed to adaptively adjust the channel sensitivity of target features at different scales. Experiments on the Pascal VOC dataset show that the proposed method improves the detection performance by 3% compared with the baseline method in terms of mean average precision (mAP).