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Open Access

ARTICLE

Enhanced Multi-Scale Object Detection Algorithm for Foggy Traffic Scenarios

Honglin Wang1, Zitong Shi2,*, Cheng Zhu3
1 School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China
3 School of Electrical & Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
* Corresponding Author: Zitong Shi. Email: email
(This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.058474

Received 13 September 2024; Accepted 20 November 2024; Published online 09 December 2024

Abstract

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.

Keywords

Deep learning; object detection; foggy scenes; traffic detection; YOLOv8

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