REFERENCES
1. Sun, L.; Chen, S.; Yao, X.; Zhang, Y.; Tao, Z.; Liang, P. Image enhancement methods and applications for target recognition in intelligent mine monitoring. J. China. Coal. Soc. 2024, 49, 495-504.
2. Shorten, C.; Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big. Data. 2019, 6, 197.
3. Xu, M.; Yoon, S.; Fuentes, A.; Park, D. S. A comprehensive survey of image augmentation techniques for deep learning. Pattern. Recognit. 2023, 137, 109347.
4. Nagaraju, M.; Chawla, P.; Kumar, N. Performance improvement of deep learning models using image augmentation techniques. Multimed. Tools. Appl. 2022, 81, 9177-200.
5. Zhuang, P.; Wu, J.; Porikli, F.; Li, C. Underwater image enhancement with hyper-laplacian reflectance priors. IEEE. Trans. Image. Process. 2022, 31, 5442-55.
6. Khalifa, N. E.; Loey, M.; Mirjalili, S. A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif. Intell. Rev. 2022, 55, 2351-77.
7. Gupta, H.; Kotlyar, O.; Andreasson, H.; Lilienthal, A. J. Robust object detection in challenging weather conditions. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, USA. Jan 03-08, 2024. IEEE; 2024. pp. 7508-17.
8. Wang, M.; Bao, J.; Zhang, Q.; et al. Research on target recognition algorithm and track joint detection method for unmanned monorail crane working in underground coal mine. J. China. Coal. Soc. 2024, 49, 457-71.
9. Lv, Y.; Zhou, Y.; Chen, Q.; Chi, W.; Sun, L.; Yu, L. YOLO_SRv2: an evolved version of YOLO_SR. Eng. Appl. Artif. Intell. 2024, 130, 107657.
10. Dai, Y.; Liu, W.; Wang, H.; Xie, W.; Long, K. YOLO-former: marrying YOLO and Transformer for foreign object detection. IEEE. Trans. Instrum. Meas. 2022, 71, 1-14.
11. Zhao, G.; Ding, J. Research on multi-modal image target recognition based on asynchronous depth reinforcement learning. Aut. Control. Comp. Sci. 2022, 56, 253-60.
12. Cao, X.; Su, Y.; Geng, X.; Wang, Y. YOLO-SF: YOLO for fire segmentation detection. IEEE. Access. 2023, 11, 111079-92.
13. Gomaa, A.; Minematsu, T.; Abdelwahab, M. M.; Abo-Zahhad, M.; Taniguchi, R. Faster CNN-based vehicle detection and counting strategy for fixed camera scenes. Multimed. Tools. Appl. 2022, 81, 25443-71.
14. Tran, D. Q.; Aboah, A.; Jeon, Y.; Shoman, M.; Park, M.; Park, S. Low-light image enhancement framework for improved object detection in fisheye lens datasets. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, USA. Jun 17-18, 2024. IEEE; 2024. pp. 7056-65.
15. Jiang, Y.; Gong, X.; Liu, D.; et al. EnlightenGAN: deep light enhancement without paired supervision. IEEE. Trans. Image. Process. 2021, 30, 2340-9.
16. Rasheed, A. F.; Zarkoosh, M. YOLOv11 optimization for efficient resource utilization. J. Supercomput. 2025, 81, 7520.
18. Wang, J.; Yang, P.; Liu, Y.; et al. Research on improved YOLOv5 for low-light environment object detection. Electronics 2023, 12, 3089.
19. Alif, M. A. R. YOLOv11 for vehicle detection: advancements, performance, and applications in intelligent transportation systems. arXiv 2024, arXiv:2410.22898. https://doi.org/10.48550/arXiv.2410.22898. (accessed 29 Jul 2025).
20. Cai, Y.; Luan, T.; Gao, H.; et al. YOLOv4-5D: an effective and efficient object detector for autonomous driving. IEEE. Trans. Instrum. Meas. 2021, 70, 1-13.
21. Wu, Z.; Guo, K.; Wang, L.; Hu, M.; Ren, S. A collaborative learning-based urban low-light small-target face image enhancement method. ACM. Trans. Sen. Netw. 2023.
22. Hao, S.; Wang, Z.; Sun, F. LEDet: a single-shot real-time object detector based on low-light image enhancement. Comput. J. 2021, 64, 1028-38.
23. Hu, J.; Shen, L.; Sun, G. Squeeze-and-excitation networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA. Jun 18-23, 2018. IEEE; 2018. pp. 7132-41.
24. Zhang, M.; Ye, S.; Zhao, S.; Wang, W.; Xie, C. Pear object detection in complex orchard environment based on improved YOLO11. Symmetry 2025, 17, 255.