Penerapan Deteksi Titik Api Pada Area graving dock Menggunakan YOLO dan GRAD-CAM - Fadlol, Muhammad Thoriq; Khumaidi, Agus; Subiyanto, Lilik; Joko Endrasmono; Mustika Kurnia Mayangsari; Anggarjuna Puncak Pujiputra (Program Studi Teknik Elektronika Politeknik Negeri Malang, 2025)
Fire spot detection in the graving dock area is crucial to prevent potentially harmful fires. This study employs the YOLOs method as a deep learning-based object detection technique to detect fire and sparks in real-time. Despite its high accuracy, visual interpretation of detection results remains challenging. Therefore, the Grad-CAM technique is utilized to generate a heatmap on the detection area of YOLO. The heatmap is calculated using the alpha blending method with a specific transparency factor, resulting in clearer visualization of detected objects. The test results show that the combination of YOLO and Grad-CAM can detect fire with an accuracy of 73%. The heatmap visualization validates the critical areas that contribute to the model's decision, making it suitable for fire monitoring systems in high-risk areas.