Research on automatic recognition technology for trucks and pickups based on improved YOLOv7

Peidong He

doi:10.1117/12.3049913

8 November 2024 Research on automatic recognition technology for trucks and pickups based on improved YOLOv7

Peidong He

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341639 (2024) https://doi.org/10.1117/12.3049913
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

As the evolution of intelligent transportation systems progresses, the need for the automated identification and categorization of commercial vehicles is becoming more critical in areas such as traffic surveillance, logistics management, and security prevention. This publication presents a refined YOLOv7-driven approach for the automatic identification of trucks and pickup trucks, designed to tackle the deficiencies in existing detection frameworks for spotting diminutive and similarly-classified targets. Initially, this article dissects the architecture of YOLOv7, pinpointing the pivotal spots for enhancement. On this basis, The YOLOv7-BCI model enhanced in this paper boosts the model's efficacy by integrating the Convolutional Block Attention Module (CBAM), the Spatial Pyramid Pooling Layer (SPPF), and the incorporation of Involutional blocks; Simultaneously replacing CIoU with SIoU optimized the regression loss function. The empirical outcomes demonstrate that the improved algorithmic framework is effective on the image datasets of homemade trucks and pickups mAP@0.5 The indicator has reached 81.1%, an increase of 2.7% compared to the original model, which basically meets the needs of commercial vehicle classification and detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Peidong He "Research on automatic recognition technology for trucks and pickups based on improved YOLOv7", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341639 (8 November 2024); https://doi.org/10.1117/12.3049913

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KEYWORDS

Object detection

Detection and tracking algorithms

Performance modeling

Intelligence systems

Education and training

Systems modeling

Mathematical optimization

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