Image-Based Outlet Fire Causing Classification Using CNN-Based Deep Learning Models

Accidents resulting from fires caused by electrical devices are frequent occurrences, inflicting substantial damage to both human lives and infrastructure in the Republic of Korea. To ascertain whether these fires stem from external or internal infrastructure factors, investigators such as the police, The National Institute of Scientific Investigation, and the National Fire Research Institute conduct fire-causing inspections. However, obtaining conclusive results in this regard is an intricate process, exacerbated by the dearth of adequate digital forensics tools and related programs. Among electrical devices, multi-socket outlets also contribute to fire incidents. This study explores the feasibility of employing CNN-based deep learning object detection models for fire-causing inspection systems targeting multi-socket outlets. Specifically, we introduce a novel image dataset comprising 6009 images of post-fire multi-socket outlets remaining, categorized into two classes: "burnt-in" and "burnt-out." This dataset is utilized for training various models, including the YOLO-series (v5, v6, and v8), Faster-RCNN, RetinaNet, and SSD. Results from our experiments show the feasibility of six CNN models in detecting the cause of fire in post-fire sockets. Particularly, YOLOv5s surpasses other models with an accuracy of 89.1% mAP@0.5, a model size of 14.4MB, and an inference time of 44.5ms (equivalent to 22 fps) on RTX 3050. Subsequently, the trained models are implemented in an operational application for trial testing during an executive period.