References
[1]. Zou, Zhengxia, et al. "Object detection in 20 years: A survey." Proceedings of the IEEE (2023).
[2]. Zaidi, Syed Sahil Abbas, et al. "A survey of modern deep learning based object detection models." Digital Signal Processing (2022): 103514.
[3]. Niederlöhner, Daniel, et al. "Self-supervised velocity estimation for automotive radar object detection networks." 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022.
[4]. Gonzales-Martínez, Rosa, et al. "Hyperparameters tuning of faster R-CNN deep learning transfer for persistent object detection in radar images." IEEE Latin America Transactions 20.4 (2022): 677-685.
[5]. Wei, Zhiqing, et al. "Mmwave radar and vision fusion for object detection in autonomous driving: A review." Sensors 22.7 (2022): 2542.
[6]. Zhou, Taohua, et al. "Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection." IEEE Transactions on Intelligent Vehicles (2023).
[7]. Hwang, Jyh-Jing, et al. "CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection." Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVIII. Cham: Springer Nature Switzerland, 2022.
[8]. Diwan, Tausif, G. Anirudh, and Jitendra V. Tembhurne. "Object detection using YOLO: Challenges, architectural successors, datasets and applications." Multimedia Tools and Applications (2022): 1-33.
[9]. Wang, Yi, et al. "Remote sensing image super-resolution and object detection: Benchmark and state of the art." Expert Systems with Applications (2022): 116793.
[10]. Liang, Tingting, et al. "Cbnet: A composite backbone network architecture for object detection." IEEE Transactions on Image Processing 31 (2022): 6893-6906.
[11]. Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
[12]. Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE international conference on computer vision. 2015.
[13]. Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems 28 (2015).
[14]. Vicente, Sara, et al. "Reconstructing pascal voc." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
[15]. Lin, Tsung-Yi, et al. "Microsoft coco: Common objects in context." Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. Springer International Publishing, 2014.
Cite this article
Cao,S. (2023). Review of Object Detection Challenges in Autonomous Driving. Applied and Computational Engineering,8,707-713.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Zou, Zhengxia, et al. "Object detection in 20 years: A survey." Proceedings of the IEEE (2023).
[2]. Zaidi, Syed Sahil Abbas, et al. "A survey of modern deep learning based object detection models." Digital Signal Processing (2022): 103514.
[3]. Niederlöhner, Daniel, et al. "Self-supervised velocity estimation for automotive radar object detection networks." 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022.
[4]. Gonzales-Martínez, Rosa, et al. "Hyperparameters tuning of faster R-CNN deep learning transfer for persistent object detection in radar images." IEEE Latin America Transactions 20.4 (2022): 677-685.
[5]. Wei, Zhiqing, et al. "Mmwave radar and vision fusion for object detection in autonomous driving: A review." Sensors 22.7 (2022): 2542.
[6]. Zhou, Taohua, et al. "Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection." IEEE Transactions on Intelligent Vehicles (2023).
[7]. Hwang, Jyh-Jing, et al. "CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection." Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVIII. Cham: Springer Nature Switzerland, 2022.
[8]. Diwan, Tausif, G. Anirudh, and Jitendra V. Tembhurne. "Object detection using YOLO: Challenges, architectural successors, datasets and applications." Multimedia Tools and Applications (2022): 1-33.
[9]. Wang, Yi, et al. "Remote sensing image super-resolution and object detection: Benchmark and state of the art." Expert Systems with Applications (2022): 116793.
[10]. Liang, Tingting, et al. "Cbnet: A composite backbone network architecture for object detection." IEEE Transactions on Image Processing 31 (2022): 6893-6906.
[11]. Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
[12]. Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE international conference on computer vision. 2015.
[13]. Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems 28 (2015).
[14]. Vicente, Sara, et al. "Reconstructing pascal voc." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
[15]. Lin, Tsung-Yi, et al. "Microsoft coco: Common objects in context." Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. Springer International Publishing, 2014.