| Περιγραφή: |
In computer vision applications, image detection is essential because it makes it possible to automatically identify objects in images and videos. The efficiency of the machine learning-based object identification technique using Haar Cascades makes it popular for real-time detection. Grayscale image detection is essential in scenarios where color information is unnecessary or unavailable, such as medical imaging, military surveillance, and low-light environments. The proposed approach detects objects in grayscale photos using OpenCV'sHaar Cascades classifier. To reduce false positives and improve detection accuracy, a number of parameters are adjusted, including scale factor, minimum neighbours, and min/max size. Several grayscale images are used to test the system and evaluate its performance in various scenarios. Based on our experiment, parameter adjustment reduces false positive detections while preserving computational efficiency and greatly increases detection accuracy. The model shows the benefits of fine-tuning over default settings by successfully detecting objects in a range of lighting situations and scales. For increased accuracy, future developments are going to include deep learning-based object identification models like CNNs or YOLO. Additionally, performance can be further optimized for real-world applications in automation, medical imaging, and surveillance by growing the dataset and implementing real-time processing algorithms. |