DeepCounter: Using Deep Learning to Count Garbage Bags
DeepCounter: Using Deep Learning to Count Garbage Bags
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DOI：
10.1109/rtcsa.2018.00010
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
K. Mikami;Yin Chen;J. Nakazawa;Yasuhiro Iida;Yasunari Kishimoto;Yuma Oya
中科院分区：
文献类型：
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作者：
K. Mikami;Yin Chen;J. Nakazawa;Yasuhiro Iida;Yasunari Kishimoto;Yuma Oya
本文提出了DeepCounter，一种汽车传感系统，其中基于深度学习的图像处理技术用于从安装在垃圾车后部的摄像头拍摄的视频中自动计数收集的垃圾袋的数量，以感测细粒空间，城市中处理的垃圾量的时间分布，其被设想为有助于开发与垃圾收集相关的新颖应用那里一个原型系统上实现的GPU集成的信号板计算机。基于单次多盒检测器（SSD），一种著名的实时目标检测算法的检测跟踪计数（DTC）算法的开发和实现。实验评估验证了所提出的方法的可行性，使用视频的现实垃圾收集在藤泽市，日本。
This paper proposes DeepCounter, an automotive sensing system where deep learning based image processing technology is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck in order to sense a fine-grain spatio-temporal distribution on the amount of disposed garbage in cities that is envisioned to be helpful to develop novel applications related to garbage collection there. A prototype system is implemented on a GPU-integrated signal-board computer. A detection-tracking-counting (DTC) algorithm is developed and implemented based on the single shot multibox detector (SSD), a well-known real-time object detection algorithm. Experimental evaluation validates the feasibility of the proposed approach using video of realistic garbage collection in Fujisawa city, Japan.