NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection

• URL: http://arxiv.org/abs/2001.06690v1
• Date: Sat, 18 Jan 2020 15:21:29 GMT
• Title: NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection
• Authors: Yazhao Li, Yanwei Pang, Jianbing Shen, Jiale Cao, Ling Shao
• Abstract summary: We propose a new Neighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramid features and explore scale-aware features. A single-shot network called NETNet is constructed for scale-aware object detection.
• Score: 170.30694322460045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the advantages of real-time detection and improved performance, single-shot detectors have gained great attention recently. To solve the complex scale variations, single-shot detectors make scale-aware predictions based on multiple pyramid layers. However, the features in the pyramid are not scale-aware enough, which limits the detection performance. Two common problems in single-shot detectors caused by object scale variations can be observed: (1) small objects are easily missed; (2) the salient part of a large object is sometimes detected as an object. With this observation, we propose a new Neighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramid features and explore scale-aware features. In NET, a Neighbor Erasing Module (NEM) is designed to erase the salient features of large objects and emphasize the features of small objects in shallow layers. A Neighbor Transferring Module (NTM) is introduced to transfer the erased features and highlight large objects in deep layers. With this mechanism, a single-shot network called NETNet is constructed for scale-aware object detection. In addition, we propose to aggregate nearest neighboring pyramid features to enhance our NET. NETNet achieves 38.5% AP at a speed of 27 FPS and 32.0% AP at a speed of 55 FPS on MS COCO dataset. As a result, NETNet achieves a better trade-off for real-time and accurate object detection.

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