SAFNet: A Semi-anchor-free Network with Enhanced Feature Pyramid for Object Detection

In recent years, the field of object detection has made significant progress. The success of most of the state-of-the-art object detectors is derived from the use of feature pyramid and the carefully designed anchor boxes. However, the current methods of constructing feature pyramid usually blindly integrate multi-scale representations on each feature hierarchy. Furthermore, these detectors also suffer from some drawbacks brought by the hand-designed anchors. To mitigate the adverse effects caused thereby, we introduce a one-stage object detector, named as the semi-anchor-free network with enhanced feature pyramid (SAFNet). Specifically, to better construct feature pyramid, we propose a novel enhanced feature pyramid generation paradigm, which mainly consists of two modules, i.e., adaptive feature fusion module (AFFM) and self-enhanced module (SEM). The paradigm adaptively integrates multi-scale representations in a non-linear method meanwhile suppress the redundant semantic information for each pyramid level, such that a clean and enhanced feature pyramid could be obtained. In addition, an adaptive anchor generator (AAG) is designed to yield fewer but more suitable anchor boxes for each input image. Benefiting from the enhanced feature pyramid, AAG is capable of generating more accurate anchor boxes by introducing few priors. Thus, AAG has the ability to alleviate the drawbacks caused by the preset anchor hyper-parameters and helps to decrease the computation cost. Extensive experiments demonstrate the effectiveness of our approach. Profited from the proposed modules, SAFNet significantly boosts the detection performance, i.e., achieving 2 points and 2.1 points higher Average Precision (AP) than RetinaNet (our baseline) on PASCAL VOC and MS COCO respectively. Codes will be publicly available soon.