Vehicle detection is essential in smart cities and intelligent transportation systems. However, current models face challenges in balancing detection accuracy and model complexity, limiting their deployment on low-performance edge devices, especially in complex scenarios with multi-scale objects and environmental variations. To overcome these issues, YOLO-MER, a novel lightweight multi-scale feature fusion vehicle detection network is proposed. First, a multi-scale feature fusion convolutional architecture, MERes Block, is developed based on an enhanced ResBlock to reconstruct the YOLO backbone, enabling feature extraction and fusion across four scales. Additionally, a lightweight neck architecture is designed to integrate features across three dimensions, ensuring comprehensive utilization of multi-scale information. The proposed YOLO-MER is validated on two public datasets. On the Vehicle dataset, it achieved the highest detection accuracy with 79.05% mAP, the smallest model size with 6.94 MB, and the fewest parameters with 1.59M. On the Crack dataset, it achieved the best detection accuracy with 86.11% mAP.

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