Local Enhancement and Bidirectional Feature Refinement Network for Single-Shot Detector

Benefit from multi-scale feature pyramid methods, recently single-stage object detectors have achieved promising accuracy and fast inference speed. However, the majority of existing feature pyramid detection techniques only simply describe complex contextual relationships from different scales. Not only are there no effective modules that adaptively extend appropriate semantic information from deeper layers, but the finer spatial localization cues from lower layers are often ignored. In this paper, we present a Local Enhancement and Bidirectional Feature Refinement Network (LFBFR), which includes two optimization methods to achieve remarkable improvements in detection accuracy. Firstly, to make the backbone more suitable for detection task, we modify the pre-trained classification backbone to mitigate the loss of details in small objects due to consecutive decrease of the image resolution. Then we propose a Bidirectional Feature Refinement Pyramid, which can effectively utilize the inter-channel relationship of higher-level features and fine appearance cues from lower-level features by using the attention residual refinement module and the feature reuse module. Ultimately, to assess the performance of the proposed LFBFR, we design a powerful end-to-end single-stage detector called LFBFR-SSD by embedding it into the framework of SSD. Extensive experiments on the PASCAL VOC and MS COCO verify that our LFBFR-SSD outperforms a lot of state-of-the-art detectors while maintaining a real-time speed.