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http://dx.doi.org/10.12815/kits.2022.21.3.83

Application of Deep Learning-based Object Detection and Distance Estimation Algorithms for Driving to Urban Area  

Seo, Juyeong (Dept. of Electronics, Korea National Univ. of Transportation)
Park, Manbok (Dept. of Electronics, Korea National Univ. of Transportation)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.21, no.3, 2022 , pp. 83-95 More about this Journal
Abstract
This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.
Keywords
Self-driving; Deep-learning; Object-detection; Distance-estimation; Camera;
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