Accurate detection of car parts is essential for applications in intelligent transportation systems, automated vehicle inspection, and maintenance planning. However, varying object scales, background clutter, and occlusions still hinder reliable real-time detection. To address these challenges, this paper presents an enhanced YOLO-based architecture that integrates task-specific feature refinement and improved supervision strategies for fine-grained car-part detection. The framework employs a modified C2fCIB block for enriched cross-channel feature interaction and multi-scale representation, an improved PSA module with progressive selective filtering for discriminative spatial-channel attention, and an SPPF layer for efficient multi-receptive field context extraction. In addition, two newly introduced components such as SCDown, a spatial-channel downsampling module designed to retain semantic richness during resolution reduction, and a Dual Assignment Head combining One-to-One and One-to-Many label assignment to further enhance the small-part sensitivity, localization robustness, and recall. Experimental results on a car-parts dataset demonstrate that the proposed model achieves a precision of 63.3%, recall of 81.6%, mAP of 73.7%, and an inference speed of 111 FPS, outperforming baseline detectors including Faster R-CNN, SSD, YOLOv4, YOLOv5, YOLOv7, and YOLOv8. The findings confirm that the proposed architecture delivers an effective balance of accuracy and efficiency, making it suitable for real-world automotive inspection and intelligent vehicle applications.
Chandaluri et al. (Wed,) studied this question.