An improved garbage classification detection algorithm based on YOLOv8

TANG Mengyu; ZENG Zhilin; WEN Yong

doi:10.13205/j.hjgc.202504011

Volume 43 Issue 4

Apr. 2025

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TANG Mengyu, ZENG Zhilin, WEN Yong. An improved garbage classification detection algorithm based on YOLOv8[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(4): 110-120. doi: 10.13205/j.hjgc.202504011

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TANG Mengyu, ZENG Zhilin, WEN Yong. An improved garbage classification detection algorithm based on YOLOv8[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(4): 110-120. doi: 10.13205/j.hjgc.202504011

TANG Mengyu, ZENG Zhilin, WEN Yong. An improved garbage classification detection algorithm based on YOLOv8[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(4): 110-120. doi: 10.13205/j.hjgc.202504011

Citation:

TANG Mengyu, ZENG Zhilin, WEN Yong. An improved garbage classification detection algorithm based on YOLOv8[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(4): 110-120. doi: 10.13205/j.hjgc.202504011

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An improved garbage classification detection algorithm based on YOLOv8

doi: 10.13205/j.hjgc.202504011

School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China

Received Date: 2024-10-15
Accepted Date: 2024-12-15
Rev Recd Date: 2024-11-08

Available Online: 2025-06-07

Publish Date: 2025-04-01

Abstract

Abstract

To address the problems of the low accuracy for dense targets and are easily interfered by the environmental background in current garbage detection algorithms， this paper proposed a garbage detection algorithm YOLOv8-MA based on the improvement of YOLOv8. The method integrated the GAM （Global Attention Mechanism） and the MLCA （Mixed Local Channel Attention） mechanism into the YOLOv8 backbone network to enhance the capacity to capture regional context information. The GAM global attention mechanism enhanced the model's capacity to perceive and utilize global information by reducing information loss and amplifying global interactions. The MLCA mixed local channel attention mechanism focused more precisely on local feature representations， which improved the model's detection capacity to distinguish stacked garbage under complex backgrounds for achieving a significant improvement in the detection accuracy of garbage classification detection tasks， perform global attention on the input feature map and pay more attention to pixel-level information in higher-level feature maps， effectively capture the cross-dimensional interactions and establish the dependency relations between dimensions to achieve a significant improvement in detection accuracy. In addition， the algorithm adopted the EIoU loss function to enhance the adaptability of the model to adapt to various samples by optimizing the regression process of the bounding box， accelerating the convergence speed of the model for locating garbage targets， enhancing the model's detection capacity to distinguish stacked garbage under complex backgrounds. The experiments conducted on TACO datasets had achieved good results， the results showed that compared with the baseline model YOLOv8， YOLOv8-MA had improved the precision， recall， mAP@0.5 and mAP@0.5∶0.95 evaluation indicators by 1.4%， 5.3%， 4.2% and 5.4%， respectively， showing an outstanding performance and having a better detection effect. The experimental results proved the effectiveness and excellence of the YOLOv8-MA algorithm. In addition， the experiment of ablation proved the effectiveness of each improvement module in enhancing the model performance.
- object detection,
- YOLOv8,
- garbage detection,
- attention mechanisms,
- deep learning

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References(23)

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