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

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 [email protected] 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.