Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCALVOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some smallobjects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS. Code is available at https://github.com/wnzhyee/Feature-Fused-SSD. Keywords: small object detection, feature fusion, real-time, single shot multi-box detector
翻译:在计算机视野中,小型物体探测是一项具有挑战性的任务,因为其分辨率和信息有限。为了解决这个问题,大多数现有方法都牺牲了提高准确性的速度。在本文件中,我们的目标是以快速的速度探测小物体,使用最佳物体探测器单一射击多箱探测器(SSD),作为基准结构,精确性Vs-速度交换。我们建议了一种多级特征聚合方法,用于在SD引入背景信息,以提高小物体的准确性。在详细的聚合操作中,我们设计了两个特性组合模块,即聚合模块和元素和元素组合模块,这与添加背景信息的方式不同。实验结果表明,这两个聚变模块在PASCALVOC2007上分别获得比基线SSDD高1.6和1.7点的 mAP,特别是某些小目标类别的2-3点改进。测试速度分别为43和40 FPS,优于艺术进化单射线探测器(DSDSD)的状态,29.4和26.4 FPS。