Enhancing Face Detection Performance under Various Illumination Conditions using YOLO and Haar Cascade with Canny Edge Detection
Keywords:
face detection, YOLO, Haar Cascade, Canny Edge Detection, varying illumination conditionsAbstract
The primary challenge in face detection is performance instability under varying illumination conditions, particularly in low-light environments. This research aims to enhance face detection accuracy under high, medium, and low lighting conditions by integrating YOLO, Haar Cascade, and Canny Edge Detection. Canny Edge Detection is employed as a pre-processing step to emphasize facial contours and mitigate illumination effects prior to model input. Performance evaluation was conducted using standard metrics, including Accuracy, Precision, and Recall, on a dataset of 1,500 images. Experimental results indicate that the integration of YOLO with Canny Edge Detection (YOLO + Canny) significantly outperformed the other methods, achieving the highest average Accuracy of (92.0%) under medium lighting conditions, while maintaining high performance under high light (91.0%) and low light (87.2%). Furthermore, the YOLO + Canny approach demonstrated the lowest processing time, at less than 0.2 seconds per image. In conclusion, the integration of YOLO and Canny Edge Detection provides high robustness, speed, and accuracy for face detection across varying illumination conditions, demonstrating strong potential for real-world applications.
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