Evaluating Pretrained YOLO11s Vehicle Detection Under Daylight and Simulated Nighttime Conditions Using Lightweight Image Enhancement
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Vehicle detection is an important perception task in Intelligent Transportation Systems (ITS) and Advanced Driver Assistance Systems (ADAS), and the performance under different illumination conditions still remains as an important challenge. The current deep-learning object detectors perform well under daylight conditions, while the detection behavior may be degraded in nighttime and low-light conditions due to low contrast and limited object visibility. This paper introduces a lightweight and reproducible framework for evaluating the pre-trained YOLO11s object detector in daylight and simulated night-time scenarios without any further retraining of the model. A typical road-scene frame from an actual daytime driving video was used to produce a corresponding simulated nighttime image with the same road geometry, vehicle locations, and camera viewpoint. The vehicle detection was preceded by enhancing the nighttime image using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction. Detection behavior was evaluated by the number of detected vehicles, confidence-score statistics, confidence-level distribution, confidence ranking, scatter visualization, confidence-weighted heatmaps, and exploratory Pearson correlation analysis. The experimental results showed that YOLO11s detected 10 vehicles during daylight conditions and 3 vehicles under simulated nighttime conditions, which is about 70% less detections under reduced illumination. This is a decrease, but the most visually prominent vehicle still had a high confidence score in both conditions, while a number of weaker detections, including a dark-colored vehicle, were lost in the simulated nighttime scene. The proposed framework provides a simple and reproducible way to study the effect of illumination on the detection behavior of pretrained YOLO11s and highlights the need for further validation on larger datasets and real nighttime driving scenes.
S. Grigorescu, B. Trasnea, T. Cocias, G. Macesanu, “A survey of deep learning techniques for autonomous driving,” Journal of Field Robotics, vol. 37, no. 3, pp. 362–386, 2020.
https://doi.org/10.1002/rob.21918
E. Yurtsever, J. Lambert, A. Carballo, K. Takeda, “A survey of autonomous driving: Common practices and emerging technologies,” IEEE Access, vol. 8, pp. 58443–58469, 2020.
https://doi.org/10.1109/ACCESS.2020.2983149
M. A. Berwo, A. Khan, Y. Fang, H. Fahim, S. Javaid, J. Mahmood, Z. U. Abideen, and S. M. S., “Deep learning techniques for vehicle detection and classification from images/videos: A survey,” Sensors, vol. 23, no. 10, Art. no. 4832, 2023.
https://doi.org/10.3390/s23104832
P. Viktor and G. Kiss, “Sensors in self-driving vehicles: A detailed literature review and new trends,” Sensors, vol. 26, no. 7, Art. no. 2153, 2026.
https://doi.org/10.3390/s26072153
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020.
https://doi.org/10.48550/arXiv.2004.10934
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 7464–7475.
J. Terven, D.-M. Córdova-Esparza, and J.-A. Romero-González, “A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Machine Learning and Knowledge Extraction, vol. 5, no. 4, pp. 1680–1716, 2023.
https://doi.org/10.3390/make5040083
M. Chaman, A. El Maliki, N. Jariri, A. El Mrabet, H. Dahou, H. Laamari, and A. Hadjoudja, “A real-time vehicle detection system for ADAS in autonomous vehicles using YOLOv11 deep neural network on embedded edge platforms,” Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 28077–28082, 2025. https://doi.org/10.48084/etasr.12138
B. Omodaratan, A. Jamali, T. Wiley, Z. Al-Saadi, R. Mallipeddi, E. Asadi, H. Asadi, R. Sadeghian, S. Sareh, and H. Khayyam, “Advances in You Only Look Once (YOLO) algorithms for lane and object detection in autonomous vehicles,” Engineering Applications of Artificial Intelligence, vol. 168, Art. no. 113893, 2026.
https://doi.org/10.1016/j.engappai.2026.113893
C. Guo, C. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, and R. Cong, “Zero-reference deep curve estimation for low-light image enhancement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1780–1789.
W. Yang, S. Wang, Y. Fang, Y. Wang, and J. Liu, “From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3063–3072. https://doi.org/10.1109/CVPR42600.2020.00313
C. Li, C. Guo, L. Han, J. Jiang, M.-M. Cheng, J. Gu, and C. C. Loy, “Low-light image and video enhancement using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9396–9416, 2022. https://doi.org/10.1109/TPAMI.2021.3126387
L. Liang, H. Ma, L. Zhao, X. Xie, C. Hua, M. Zhang, and Y. Zhang, “Vehicle detection algorithms for autonomous driving: A review,” Sensors, vol. 24, no. 10, Art. no. 3088, 2024. https://doi.org/10.3390/s24103088
J. Tian, M. A. Ghaffar, and Z. Li, “YOLO-Night: Lighting the path for autonomous vehicles with robust nighttime perception,” Sensors, vol. 26, no. 4, Art. no. 1138, 2026.
https://doi.org/10.3390/s26041138
S. Biswas, J. Kumar, A. Mitra, A. Ganesh, and P. Singh, “Multi-dimensional attention transformer for vehicle and pedestrian detection in adverse weather,” Scientific Reports, vol. 16, Art. no. 12624, 2026. https://doi.org/10.1038/s41598-026-40319-7
L. Peng and L. Jiang, “Nighttime vehicle target detection based on visual features,” Applied Computing and Intelligence, vol. 6, no. 1, pp. 23–37, 2026. https://doi.org/10.3934/aci.2026002
S. Singh, R. Kumari, P. Pallavi, and P. Saurabh, “A systematic review of deep learning methods for low-light image enhancement and object detection,” Discover Applied Sciences, vol. 8, Art. no. 241, 2026. https://doi.org/10.1007/s42452-025-08051-5
Q. Zou, H. Jiang, Q. Dai, Y. Yue, L. Chen, and Q. Wang, “Robust lane detection from continuous driving scenes using deep neural networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 41–54, 2020.
https://doi.org/10.1109/TVT.2019.2949603
Z. Qin, H. Wang, and X. Li, “Ultra fast structure-aware deep lane detection,” in A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds., Computer Vision – ECCV 2020. Cham, Switzerland: Springer, 2020, pp. 276–291. https://doi.org/10.1007/978-3-030-58586-0_17
M. K. Hasan, L. O. Alhamawndi, A. Ghazi, L. S. Ahmed, and A. J. Mohammed, “A real-time, training-free lane detection approach robust to day–night illumination changes on CPU-only platforms,” Engineering and Technology Journal, vol. 11, no. 3, pp. 9247–9253, 2026.
