التفاصيل البيبلوغرافية
| العنوان: |
Improved YOLOv7 Automatic Driving Object Detection Algorithm. (English) |
| المؤلفون: |
HU Miao, JIANG Lin, TAO Youfeng, ZHANG Zhijian |
| المصدر: |
Journal of Computer Engineering & Applications; 6/1/2024, Vol. 60 Issue 11, p165-172, 8p |
| مصطلحات موضوعية: |
OBJECT recognition (Computer vision), ALGORITHMS, AUTONOMOUS vehicles, MOTOR vehicle driving, AUTOMOBILE driving |
| مستخلص: |
It is very important for autonomous driving vehicles to accurately detect objects such as vehicles and pedestrians on the road in real time. Aiming at the problems of missed detection and poor detection effect of small targets in the autonomous driving scene, this paper proposes an automatic driving target detection algorithm that improves the YOLOv7 algorithm. Firstly, it modifies the modules in the network to expand the receptive field, reduces the size of the receptive field module, and improves the speed of the model and enhances the ability to extract image feature information. Secondly, the paper introduces the BRA attention mechanism at the output of the backbone network to improve the model's ability to small target objects. Finally, it replaces the original CIOU loss function of the algorithm with the EIOU loss function to minimize the difference between the height and width of the predicted frame and the real frame, and speeds up the convergence of the model while achieving better positioning results. The experimental results show that: on the KITTI dataset, when the improved YOLOv7 algorithm performs target detection, its mAP reaches 94.7%, which is 3.1 percentage points higher than the original YOLOv7 algorithm, and it has achieved higher detection accuracy in small target object detection. It effectively improves the model's detection effect on small targets. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
