MCDet: Multi-Content Collaboration Detector for Multiscale Remote Sensing Object
Wenping Ma, Hao Chen, Hao Zhu, Na Li, Yue Wu, Biao Hou, Licheng Jiao · IEEE Geoscience and Remote Sensing Letters · 2024
In previous works, powerful CNN backbones are typically used for one- or two-stage detectors to facilitate multi-categories object classification. Unfortunately, continuous convolution and pooling operations tend to weaken the detailed information. We propose an end-to-end Multi-content Collaboration Detector (MCDet) to improve object recognition accuracy. First, we summarize the reasons for the disappearance of detailed features in traditional feature extraction backbone networks, and propose a Shallow Clue Refinement (SCR) module, which helps us to retain more critical local detail information in the downsampling process. Second, to receive more suitable contextual information, we design a Self-dilating Spatial Pooling (SSP) module, it adaptively learns a contextual reception field, thereby alleviating the mismatch between the theoretical receptive field of the network design and the practical requirements. Finally, extensive experiments on the NWPU VHR-10 and DIOR datasets have shown that the proposed MCDet significantly improves detection accuracy. Our code is available at https://github.com/Xidian-AIGroup190726/RS-objectdetection-MCDet.