DESIGN AND EVALUATION OF A DEEP LEARNING-BASED COMPUTER VISION MODEL FOR AUTOMATED OBJECT DETECTION

This project addresses the need for intelligent systems in modern applications like surveillance, autonomous driving, healthcare imaging, and smart retail that can automatically detect objects in visual data. Traditional computer vision methods, which rely on handcrafted features, often struggle with challenges posed by complex environments such as variations in lighting, scale, and occlusion. To overcome these limitations, the project designs and evaluates a deep learning- based computer vision model for automated object detection. The system leverages Convolutional Neural Networks (CNNs) to automatically learn meaningful feature representations from annotated image datasets. It detects and localizes multiple objects by generating bounding boxes, class labels, and confidence scores. Performance and robustness are enhanced through techniques including data preprocessing, augmentation, transfer learning, and optimized training strategies. The model’s effectiveness is evaluated using metrics such as mean Average Precision (mAP), precision, recall, and inference time. Experimental results show that the proposed system achieves reliable accuracy and efficiently manages complex scenes containing multiple objects. This work demonstrates the power of deep learning to improve object detection performance and supports practical applications in automated monitoring, safety systems, and intelligent analytics.