Abstract
Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.
Highlights
At present, garbage classification has received great attention
(i) A hardware deployment scheme based on the image processing unit with network processing unit (NPU) is proposed (ii) Based on the analysis of the image characteristics of the garbage can, a dataset for garbage can recognition is established by using the garbage can color as the classification basis (iii) An intelligent supervision and workload statistics algorithm for garbage trucks is proposed based on the trained YOLOv3-tiny model
There is a monitoring and statistical method for garbage trucks in Zhonglian Environment, which is achieved by installing RFID tags with specific information on the bottom of each garbage can
Summary
Garbage classification has received great attention. Garbage classification can play the role of environmental protection and can recycle and reuse some resources, which has high social benefits. Due to the lack of effective regulatory means, there is a phenomenon that on-board staffs do not follow the regulations and pour other types of garbage into their trucks. This paper attempts to use computer vision technology to supervise and count the workloads of the garbage trucks. (i) A hardware deployment scheme based on the image processing unit with NPU is proposed (ii) Based on the analysis of the image characteristics of the garbage can, a dataset for garbage can recognition is established by using the garbage can color as the classification basis (iii) An intelligent supervision and workload statistics algorithm for garbage trucks is proposed based on the trained YOLOv3-tiny model.
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