CMCA-YOLO: A Study on a Real-Time Object Detection Model for Parking Lot Surveillance Imagery

Abstract

In the accelerated phase of urbanization, intelligent surveillance systems play an increasingly pivotal role in enhancing urban management efficiency, particularly in the realm of parking lot administration. The precise identification of small and overlapping targets within parking areas is of paramount importance for augmenting parking efficiency and ensuring the safety of vehicles and pedestrians. To address this challenge, this paper delves into and amalgamates cross-attention and multi-spectral channel attention mechanisms, innovatively designing the Criss-cross and Multi-spectral Channel Attention (CMCA) module and subsequently refining the CMCA-YOLO model, specifically optimized for parking lot surveillance scenarios. Through meticulous analysis of pixel-level contextual information and frequency characteristics, the CMCA-YOLO model achieves significant advancements in accuracy and speed for detecting small and overlapping targets, exhibiting exceptional performance in complex environments. Furthermore, the study validates the research on a proprietary dataset of parking lot scenes comprising 4502 images, where the CMCA-YOLO model achieves an mAP@0.5 score of 0.895, with a pedestrian detection accuracy that surpasses the baseline model by 5%. Comparative experiments and ablation studies with existing technologies thoroughly demonstrate the CMCA-YOLO model’s superiority and advantages in handling complex surveillance scenarios.