Advancing automated street crime detection: a drone-based system integrating CNN models and enhanced feature selection techniques

This study presents a pioneering solution to the growing challenge of escalating global crime rates through the introduction of an automated drone-based street crime detection system. Leveraging advanced Convolutional Neural Network (CNN) models, the system integrates several key components for analyzing images captured by drones. Initially, the Embedding Bilateral Filter (EBF) technique divides images into base and detail layers to enhance detection accuracy. The fusion model, IR with attention-based Conv-ViT, combines Inception-V3, ResNet-50, and Convolution Vision Transformer (Conv-ViT) to capture both shape and texture details efficiently. Further enhancement is achieved through the Improved Shark Smell Optimization Algorithm (ISSOA), which optimizes feature selection and minimizes redundancy in image extraction. Additionally, a Multi-scale Contextual Semantic Guidance Network (MCS-GNet) ensures robust image classification by integrating features from multiple layers to prevent data loss. Evaluation on the UCF-Crime and UCSD Ped2 datasets demonstrates superior accuracy, with remarkable results of 0.783 and 0.974, respectively. This innovative approach offers a promising solution to the arduous and continuous task of monitoring security camera feeds for suspicious activities, thereby addressing the pressing need for automated crime detection systems on a global scale.