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.

翻译：车辆检测，特别是通过摄像头视觉的检测，已成为计算机视觉领域的主要焦点并被广泛采用。尽管当前的车辆检测系统能够实现良好的检测效果，但由于车辆间距、光照强度和环境能见度等因素，可靠的检测仍具有挑战性。为解决这些问题，我们提出了集成卷积块注意力机制的跨域车辆检测模型（CDMA），并将其应用于自动驾驶及其他领域的车辆识别。CDMA包含多项创新：1）构建完整的跨域目标检测框架；2）开发了集成卷积注意力机制的未配对目标域图像生成模块，该模块特别强调车灯特征；3）采用广义交并比（GIOU）作为目标检测框架的损失函数；4）设计了集成双头卷积块注意力模块（CBAM）的目标检测模型；5）采用有效的数据增强方法。为评估模型效果，我们对SSLAD数据集中的数据进行降分辨率处理，并将其作为实验的基准数据集。实验结果表明，相较于未使用我们框架的模型，跨域车辆目标检测模型的性能提升了40%，且我们的改进对跨域车辆识别具有显著影响。