ECNet: Edge-aware Context-aggregation Network for Transparent and Reflective Object Detection
Zhibin Xiao, Pengwei Xie, Guijin Wang · 2021
Highly transparent and reflective objects are prevalent in life, yet their existence severely degrades the performance of existing object detectors. In this paper, we propose a novel Edge-aware Context-aggregation Network (ECNet) for transparent and reflective object detection, with three well-designed modules introduced to enhance the ability of extracting distinctive features. Specially, we develop the Edge Detection Module (EDM) to make the network pay more attention to the boundary areas; present the Depth Feature Fusion Module (DFFM) to extract content discontinuity in depth maps; and introduce the Multi-respective-field Feature Extraction Module (MFEM) which fusing multi-receptive-field features to reveal the texture discontinuity. Extensive experiments show the proposed ECNet surpasses the state-of-the-art detectors by at least 7.4% mAP in transparent and reflective object detection, which well demonstrates the effectiveness of the proposed method.