OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images

• URL: http://arxiv.org/abs/2304.11805v1
• Date: Mon, 24 Apr 2023 03:30:00 GMT
• Title: OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images
• Authors: Xuexue Li, Wenhui Diao, Yongqiang Mao, Peng Gao, Xiuhua Mao, Xinming Li and Xian Sun
• Abstract summary: Occlusion between objects is one of the overlooked challenges for object detection in UAV images. We introduce the occlusion-guided multi-task network (OGMN) to address this challenge. Our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms the baseline by 5.3%.
• Score: 13.90359920041577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confusion problem and local aggregation characteristic. And we found that extracting or localizing occlusion between objects is beneficial for the detector to address this challenge. According to this finding, the occlusion localization task is introduced, which together with the object detection task constitutes our occlusion-guided multi-task network (OGMN). The OGMN contains the localization of occlusion and two occlusion-guided multi-task interactions. In detail, an occlusion estimation module (OEM) is proposed to precisely localize occlusion. Then the OGMN utilizes the occlusion localization results to implement occlusion-guided detection with two multi-task interactions. One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head. Another interaction for guide is designed in the detection process according to local aggregation characteristic, and a two-phase progressive refinement process (TPP) is proposed to optimize the detection process. Extensive experiments demonstrate the effectiveness of our OGMN on the Visdrone and UAVDT datasets. In particular, our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms the baseline by 5.3%. And our OGMN provides a new insight for accurate occlusion localization and achieves competitive detection performance.

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