An Improved Soft-YOLOX for Garbage Quantity Identification

Junran Lin, Cuimei Yang, Yi Lu, Yuxing Cai, Hanjie Zhan and Zhen Zhang
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Junran Lin: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Cuimei Yang: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Yi Lu: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Yuxing Cai: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Hanjie Zhan: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China
Zhen Zhang: School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China

Mathematics, 2022, vol. 10, issue 15, 1-12

Abstract: Urban waterlogging is mainly caused by garbage clogging the sewer manhole covers. If the amount of garbage at a sewer manhole cover can be detected, together with an early warning signal when the amount is large enough, it will be of great significance in preventing urban waterlogging from occurring. Based on the YOLOX algorithm, this paper accomplishes identifying manhole covers and garbage and building a flood control system that can automatically recognize and monitor the accumulation of garbage. This system can also display the statistical results and send early warning information. During garbage identification, it can lead to inaccurate counting and a missed detection if the garbage is occluded. To reduce the occurrence of missed detections as much as possible and improve the performance of detection models, Soft-YOLOX, a method using a new detection model for counting, was used as it can prevent the occurrence of missed detections by reducing the scores of adjacent detection frames reasonably. The Soft-YOLOX improves the accuracy of garbage counting. Compared with the traditional YOLOX, the mAP value of Soft-YOLOX for garbage identification increased from 89.72% to 91.89%.

Keywords: garbage quantity identification; YOLOX; NMS; Soft-NMS (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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