Abstract
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.
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References
Hirz M, Walzel B (2018) Sensor and object recognition technologies for self-driving cars. Comput Aided Des Appl 15(4):501–508
Thuan D (2021) Evolution of yolo algorithm and yolov5: the state-of-the-art object detection algorithm
Ruan J, Wang Z (2018) An improved algorithm for dense object detection based on YOLO. In: International Conference on Computer Engineering And Networks (pp. 592–599). Springer, Cham
Zhan C, Duan X, Xu S, Song Z, Luo M (2007) An improved moving object detection algorithm based on frame difference and edge detection. In Fourth International Conference on Image and Graphics (ICIG 2007) (pp. 519–523). IEEE
Hui J (2018) Real-time object detection with yolo, yolov2 and now yolov3. medium.com/@jonathan_hui/real-time-object-detection-with-YOLO-YOLOv2-28b1b93e2088 Accessed 24 Feb 2019
Gandhi, R. (2018). R-cnn, fast r-cnn, faster r-cnn, yolo—object detection algorithms. Towards Data Science, 9
Sam DB, Surya S, Babu RV (2017) Switching convolutional neural network for crowd counting. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4031–4039). IEEE
Wang J, Lin Y, Guo J, Zhuang L (2021) SSS-YOLO: towards more accurate detection for small ships in SAR image. Remote Sens Lett 12(2):122–131
Takahashi M, Moro A, Ji Y, Umeda K (2020) Expandable YOLO: 3D object detection from RGB-D Images
Jin Y, Wen Y, Liang J (2020) Embedded real-time pedestrian detection system using YOLO optimized by LNN. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (pp. 1–5). IEEE
Dreossi T, Ghosh S, Sangiovanni-Vincentelli A, Seshia SA (2017) Systematic testing of convolutional neural networks for autonomous driving
Schröder E, Braun S, Mählisch M, Vitay J, Hamker F (2019) Feature map transformation for multi-sensor fusion in object detection networks for autonomous driving. In: Science and Information Conference (pp. 118–131). Springer, Cham
Gandhi R (2018) R-CNN, Fast R-CNN, Faster R-CNN, YOLO object detection algorithms. July 9, 2018. Retrieved September, 20, 2019
Zhang S, Wu Y, Men C, Li X (2020) Tiny YOLO optimization oriented bus passenger object detection. Chin J Electron 29(1):132–138
Adarsh P, Rathi P, Kumar M (2020) YOLO v3-tiny: object detection and recognition using one stage improved model. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 687–694). IEEE
Horzyk A, Ergün E (2020) YOLOv3 precision improvement by the weighted centers of confidence selection. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE
Wang G, Guo J, Chen Y, Li Y, Xu Q (2019) A PSO and BFO-based learning strategy applied to faster R-CNN for object detection in autonomous driving. IEEE Access 7:18840–18859
Huang Y, Chen Y (2020) Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
Zhao L, Li S (2020) Object detection algorithm based on improved YOLOv3. Electronics 9(3):537
Fink M, Liu Y, Engstle A, Schneider SA (2019) Deep learning-based multi-scale multi-object detection and classification for autonomous driving. In: Fahrerassistenzsysteme 2018 (pp. 233–242). Springer Vieweg, Wiesbaden
Horzyk A, Ergün E (2020) YOLOv3 Precision Improvement by the Weighted Centers of Confidence Selection. In: 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE
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Zaghari, N., Fathy, M., Jameii, S.M. et al. The improvement in obstacle detection in autonomous vehicles using YOLO non-maximum suppression fuzzy algorithm. J Supercomput 77, 13421–13446 (2021). https://doi.org/10.1007/s11227-021-03813-5
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DOI: https://doi.org/10.1007/s11227-021-03813-5