mlsanityData to 7 Oct 2026

Cross-Domain Car Detection Model with Integrated Convolutional Block Attention Mechanism

arXiv 2305.20055 ↗First posted 31 May 2023cs.CVcited by 5

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Abstract (arXiv)

Car detection, particularly through camera vision, has become a major focus in the field of computer vision and has gained widespread adoption. While current car detection systems are capable of good detection, reliable detection can still be challenging due to factors such as proximity between the car, light intensity, and environmental visibility. To address these issues, we propose cross-domain Car Detection Model with integrated convolutional block Attention mechanism(CDMA) that we apply to car recognition for autonomous driving and other areas. CDMA includes several novelties: 1)Building a complete cross-domain target detection framework. 2)Developing an unpaired target domain picture generation module with an integrated convolutional attention mechanism which specifically emphasizes the car headlights feature. 3)Adopting Generalized Intersection over Union (GIOU) as the loss function of the target detection framework. 4)Designing an object detection model integrated with two-headed Convolutional Block Attention Module(CBAM). 5)Utilizing an effective data enhancement method. To evaluate the model's effectiveness, we performed a reduced will resolution process on the data in the SSLAD dataset and used it as the benchmark dataset for our task. Experimental results show that the performance of the cross-domain car target detection model improves by 40% over the model without our framework, and our improvements have a significant impact on cross-domain car recognition.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 45 of its 51 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

45 of 51 references.

References · 45

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33 more
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