Multi-class Concealed Object detection system using Yolov9 Integrated with CBAM /
Mubeen Naeem Chaghtai
- 95p. Soft Copy 30cm
Smart city development places high emphasis on high-performance safety solutions to counter city security threats such as street crime and increased numbers of unconcealed and concealed objects. Although unconcealed objects are visible threats that are relatively easy to identify, their multi-class counterparts that are concealed pose high challenges given that they are not visible, especially within dense thermal grayscale scenes. Prevalent detection solutions like metal detectors and infrared cameras usually fail to correctly recognize multi-class objects, either unconcealed or concealed objects, which often result in false negatives and require human verification. This research presents an enhanced multiclass object detection model using YOLOv9 combined with Convolutional Block Attention Modules (CBAM) to improve improvement of inference time with a special emphasis on detecting concealed objects. The model embeds channel and spatial attention from CBAM within YOLOv9’s backbone and head to enhance feature representation for multi-scale object detection and efficiently handle unconcealed as well as concealed objects from thermal images. The model applies YOLOv9’s Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to streamline feature extraction and reduce loss of information with enhanced multi-target detection accuracy. The model was trained and tested on a set of 8,600 grayscale image augmentations, spanning four object classes (revolvers, tasers, knives, and guns), with strict preprocessing to keep classes balanced and semantically sound. The YOLOv9- CBAM structure obtains a mean Average Precision (mAP@0.5) of 0.954 and mAP@0.5- 0.95 of 0.948 compared to the baseline YOLOv9's mAP@0.5 of 0.944 and mAP@0.5-0.95 of 0.940, representing accuracy improvements. The CBAM-augmented model achieves 1.9% faster inference speed than the baseline, achieving optimal performance with both unconcealed and hidden object detection at optimized computation efficiency. The efficient and highly effective system best suits real-time smart city security applications like airport screening, patrol by police, and critical infrastructure security, reducing queuing time considerably while significantly enhancing automated threat detection efficiency.