Research on Classification and Detection of Waste Incineration Based on YOLOv7

1 January 2023

journal article
Published by Hans Publishers in Computer Science and Application

Vol. 13 (02), 270-280
https://doi.org/10.12677/csa.2023.132027

Abstract

In order to realize automatic garbage detection and grasping work and solve the inaccuracy and labor of the workers operating the refuse collection point, this paper uses a real-time garbage detection algorithm based on the YOLOv7 algorithm, which can detect the garbage in each partition by taking real-time photos through the camera in the garbage pool area and grasp the garbage to be grasped into the area to be incinerated in real-time through the crane jaws, effectively improving the production efficiency. The experimental results show that the algorithm of this paper has a mAP value of 87.7% in 100 rounds of training, which can better meet the needs of real garbage in real-time industrial processing.

Keywords

This publication has 7 references indexed in Scilit:

Diverse Branch Block: Building a Convolution as an Inception-like Unit
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2021
Dynamic Head: Unifying Object Detection Heads with Attentions
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2021
End-to-End Object Detection with Transformers
Published by Springer Science and Business Media LLC ,2020
Ensemble deep learning in bioinformatics
Nature Machine Intelligence, 2020
AP-Loss for Accurate One-Stage Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2019
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2019