PANetW: PANet with wider receptive fields for object detection

PANet is widely used in various object detection tasks due to its powerful feature expression ability. However, PANet’s performance in complex scenarios is subpar, with frequent object omission or misidentification. We find that the reason for this phenomenon is that the receptive field of PANet can’t cover sufficient feature information, to deal with drastic changes of source object size. In order to solve this problem, this paper adopts dilated convolution technology and applies it to each parallel branch directly following the PANet network. This method can effectively represent the feature information of objects at different scales by integrating the information from small and large receptive fields into a new feature output. We also introduce residual structure to circumvent the network degradation caused by excessive convolutions. By combining the above methods, we build a new module named PANetW (PANet with Wider Receptive Fields). Taking YOLOX-S as the baseline, we comprehensively evaluated the proposed module PANetW on two datasets, VOC2007 and MSCOCO2017. The test results show that our PANetW achieves a high level of mean average precision (AP). On the VOC2007 dataset, the AP of our PANetW improves by 4.9% to 43.0%; on the MS COCO2017 dataset, the AP of PANetW is as high as 44.3%, far exceeding the current mainstream modules. The experimental results fully demonstrate the effectiveness of our module.