Enhanced Real-Time Suspicious Object Detection with YOLO in Diverse Environmental Conditions

Key words:  Suspicious Object, Suspicious Activities, Deep Learning, Yolov8, Environmental Complexities

Abstract:

Various imaging techniques have been employed to detect suspicious objects and activities to help preserve peace and order. However, challenges such as illumination changes, occlusion, noise, and low-resolution imagery significantly hinder the effectiveness of automated detection methods. To address these issues, machine learning techniques have been applied, but they often struggle with the multiple activities detection simultaneously, leading to ambiguity and reduced accuracy. To mitigate these issues, the proposed methodology presented the YOLOv8 model for detection of suspicious objects and activities in images and video frames. To remove the environmental noise, adaptive sliding window based bilateral filter is used to remove local and global noise in input noisy images, then YOLOv8 model is trained to identify the suspicious and non-suspicious objects and activities. The performance was evaluated on suspicious object and activity dataset collected from publicly available resources such as Roboflow. Performance was measured using mean average precision (mAP) and compared to existing state-of-the-art models. The proposed model achieved an average mAP of 74.5%, which represents approximately a 13% improvement over current leading methods. Therefore, the study shows the efficacy of the proposed model to enhance the surveillance system to handle environmental complexities.