A Control-Oriented Information System for Low-Light Object Tracking Based on Multi-Level Image Enhancement and YOLOv12
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22788Keywords:
Object tracking, YOLOv12, Contrast Limited Adaptive Histogram Equalization, Lowlight image enhancement, tracking in harsh environmentAbstract
Tracking and object detection in low-light conditions is one of the most complicated problems of computer vision, as the conditions of poor light, low contrast, and the disappearance of valuable structural image characteristics in photographed images make it difficult to detect objects. These are some of the restrictions that reduce the output of detection and tracking algorithms, especially in the cases of surveillance and monitoring. To deal with this issue, it is proposed in this paper that an integrated framework can be created to improve the enhancement of low-light images and object detection through a multi-level image fusion strategy in conjunction with the YOLOv12 detector model. The suggested approach will improve low-light frames by a multi-stage algorithm including enhancement of base illumination, amplification of the details layer, and amplification of contrast with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE). Noise-sensitive mask is also included in such a way that the strength of improvements in the dark area can be controlled to avoid unnecessary increase in noise. The refined frames are then used by the YOLOv12 model as input to detect and track objects. A dataset of 79 images in seven groups that depict the scenes of different crowds was evaluated experimentally. Conditions of low light were artificially produced to mimic difficult light conditions. The performance of the enhancement was measured in Structural similarity index (SSIM) and peak signal to noise ratio (PSNR) and the performance of the detection process was measured in confidence scores and the number of detected objects. The findings of the experimental study show that the suggested framework contributes to the increase of the visual quality and object detection throughput during the low-light conditions, especially in dense crowd scenarios. The findings affirm the fact that the combination of image enhancement, combined with detection models, can be an effective means of enhancing tracking reliability and the visibility of the object in problematic settings.
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