YOLSO: You Only Look Small Object |
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Institution: | 1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;3. College of Optical Science and Engineering, Zhejiang University, Hangzhou 310007, China |
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Abstract: | Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset. |
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Keywords: | Small object detection Background-aware Granular feature aggregation Accurate location High speed |
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