计算机科学
对象(语法)
背景(考古学)
GSM演进的增强数据速率
边缘计算
人工智能
目标检测
作者
Zhibin Xiao,Pengwei Xie,Guijin Wang
出处
期刊:International Conference on Artificial Intelligence
日期:2021-06-28
卷期号:: 77-81
标识
DOI:10.1109/icaica52286.2021.9497865
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI