Adaptive Deep Learning for Complex Crime Scene Object Detection

Computer Science and Engineering

Crime scene investigations often take place in complex environments where critical evidence may be hidden, occluded, or dispersed across cluttered backgrounds. Traditional object detection methods frequently struggle with such challenges, leading to missed or inaccurate identification of key forensic elements. This study presents an Adaptive Deep Learning Framework designed for precise object detection in intricate crime scenes. By leveraging advanced Convolutional Neural Networks (CNNs), Region-Based CNNs (R-CNNs), and attention mechanisms, the proposed model dynamically adapts to varying crime scene conditions, effectively identifying objects regardless of size, orientation, or occlusion. The framework integrates multi-scale feature extraction, context-aware learning, and adaptive learning rates to enhance accuracy and robustness. Incorporating YOLOv8 and Mask R-CNN for real-time detection and instance segmentation, the system ensures high precision in object localization and classification. Extensive testing on diverse crime scene datasets demonstrates the model’s superior performance, achieving a mean Average Precision (mAP) of 92.5% while significantly reducing false positives and negatives. This adaptive approach not only streamlines forensic investigations but also minimizes human error, offering a reliable and efficient tool for law enforcement agencies. Future research will focus on expanding the system’s capabilities to 3D crime scene reconstruction and cross-domain forensic analysis.

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