ENHANCING ACCURACY IN REAL-TIME OBJECT DETECTION USING YOLOV12 MODEL WITH TRANSFORMER BASED ATTENTION MECHANISMS

This work proposes a real-time object detection model that integrates the efficiency of YOLOv12 with an attention-based Transformer to address the limitations of traditional detection systems in dynamic surveillance environments. Classical models often struggle under challenging conditions such as low light, occlusion, or rapid movement. In contrast, the proposed hybrid model leverages YOLOv12’s robust feature extraction with the attention mechanism’s ability to emphasize salient spatial and contextual features. Trained on a comprehensive real-time surveillance dataset and benchmarked against a baseline YOLOv8 model with attention enhancements, the proposed system demonstrated significant improvements. It achieved a 96% detection rate, reduced average processing time from 35 ms to 20 ms, and lowered the error rate to 10%, with results statistically significant (p = 0.015). Key performance metrics—precision, recall, and F1-score—confirmed the model’s capability to accurately detect and classify multiple objects in varying environmental conditions. The attention-based Transformer further enabled dynamic focus on critical regions, enhancing localization and classification. This study underscores the potential of combining deep convolutional and attention architectures to create cost-effective, accurate, and scalable solutions for smart surveillance, paving the way for advancements in adaptive detection and AI-driven security systems.