Research on improved YOLOv8 detection algorithm for diesel vehicle emission of black smoke

doi:10.11996/JG.j.2095-302X.2025020249

Abstract

Abstract:

The emission of black smoke from diesel vehicles is a critical and challenging issue in road traffic environmental protection enforcement. Due to the influence of complex environmental conditions, to address the limitations of existing black smoke detection methods in terms of accuracy and speed, a lightweight detection model of black smoke emission from diesel vehicles based on improved YOLOv8 was proposed. Firstly, based on the YOLOv8 backbone network, a lightweight feature extraction module C2f-FasterRep was designed to improve the feature extraction capability of the model, while the C2f-FasterRep module integrated a context-anchored attention mechanism module to capture long-range contextual information. The global average pooling and bar convolution were employed to enhance the features in the central region of the feature map, thereby improving the detection accuracy. Secondly, a new network structure was proposed in the neck part to fuse the features extracted from the backbone network. The channel attention module and dimensional matching mechanism were employed to fuse features of different scales, enhancing the model’s multi-scale feature fusion capability. Lastly, the detection head of the YOLOv8 model was optimized using a Transformer decoder structure. An intersection-parallel-ratio aware query mechanism was incorporated to optimize the decoder query, improving the model’s performance for classification and localization. In order to ensure the authenticity and effectiveness of the experiment, data were collected and tested and verified using the detection equipment deployed on a road section in Xuchang, Henan Province. The experimental results demonstrated that the mAp of the proposed method was achieved at 95.4%, the precision at 94.5%, and the recall rate at 97.5%. Compared with existing black smoke detection methods, the proposed approach exhibited higher detection accuracy and faster detection speed. The results of ablation experiments confirmed that the proposed lightweight feature extraction module, feature fusion module and detection head contributed to improving the accuracy of model detection.

Key words: object detection, black smoke detection, YOLOv8, lightweight model, attention mechanism

CLC Number:

ZHANG Lili, YANG Kang, ZHANG Ke, WEI Wei, LI Jing, TAN Hongxin, ZHANG Xiangyu. Research on improved YOLOv8 detection algorithm for diesel vehicle emission of black smoke[J]. Journal of Graphics, 2025, 46(2): 249-258.

Figures/Tables 16

References 26

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配置	参数
CPU	Intel Core i7-7700K @4.20 GHz
GPU	NVIDIA GeForce RTX 4090
内存	24 GB
操作系统	Ubuntu 18.04
深度学习框架	PyTorch 2.0
编程语言	Python3.9

配置	参数
CPU	Intel Core i7-7700K @4.20 GHz
GPU	NVIDIA GeForce RTX 4090
内存	24 GB
操作系统	Ubuntu 18.04
深度学习框架	PyTorch 2.0
编程语言	Python3.9

Model	Param/M	FLOPs/G	Precision/%	Recall/%	mAp(0.50)/%	mAp(0.50∶0.95)/%	FPS
Faster-RCNN	165.00	199.0	82.0	87.0	85.3	41.3	24
SSD	24.50	87.9	79.0	82.0	81.5	46.8	32
YOLOv5s	7.10	16.5	75.9	82.0	83.7	50.1	40
YOLOv6s	18.50	45.3	83.7	84.1	89.1	52.5	42
YOLOv8s	11.10	28.8	85.5	87.3	88.1	57.9	45
YOLO-BSD	9.27	22.2	94.5	97.5	95.4	69.7	57

Model	Param/M	FLOPs/G	Precision/%	Recall/%	mAp(0.50)/%	mAp(0.50∶0.95)/%	FPS
Faster-RCNN	165.00	199.0	82.0	87.0	85.3	41.3	24
SSD	24.50	87.9	79.0	82.0	81.5	46.8	32
YOLOv5s	7.10	16.5	75.9	82.0	83.7	50.1	40
YOLOv6s	18.50	45.3	83.7	84.1	89.1	52.5	42
YOLOv8s	11.10	28.8	85.5	87.3	88.1	57.9	45
YOLO-BSD	9.27	22.2	94.5	97.5	95.4	69.7	57

Model	Param/ M	Precision/ %	Recall/ %	mAp(0.50)/ %
YOLOv8s	11.10	82.4	78.6	81.4
YOLO-BSD	9.27	85.8	82.9	83.5