The improvement in obstacle detection in autonomous vehicles using YOLO non-maximum suppression fuzzy algorithm

Numerous changes in algorithms have been observed by object detection to enhance both speed and accuracy. In this research, we present a method to improve the behavioral clone of self-driving cars. Thus, we first create a collection of videos and information required for safe driving on different routes and conditions. The detection of obstacles is done with the proposed algorithm called YOLO non-maximum suppression fuzzy algorithm, which performs the driver reaction to obstacles with greater accuracy and more speed than the obstacles detection algorithms using the designed framework. The network is trained by the driver's performance, and hence, the output used to control the vehicle is obtained. The non-maximum suppression algorithm plays an essential role in object detection and tracking. An effective hybrid method of fuzzy and NMS algorithms is provided in this paper to improve the problem mentioned. The proposed method improves the average accuracy of the detection network. The performance of the designed algorithm was examined using two different types of KITTI data and the data collected using the personal vehicle and the data we gathered. The proposed algorithm was assessed with evaluation accuracy criteria, which revealed that the method has a higher speed (above 64.41%), a lower FPR (below 6.89%), and a lower FNR (below 3.95%) compared with the baseline YOLOv3 model. According to the loss function, the accuracy rate of the network performance is 95%, implying that we have achieved good results.

Automated summary

This research presents a method to improve the behavioral clone of self-driving cars by enhancing the detection of obstacles using a proposed algorithm called YOLO non-maximum suppression fuzzy algorithm. The hybrid method of fuzzy and NMS algorithms provided in this study improves the accuracy of the detection network, with evaluation criteria showing higher speed, lower FPR, and lower FNR compared to the baseline YOLOv3 model. The network performance accuracy rate is 95%, indicating successful results.