Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance

Abstract

Open burning is the main factor contributing to the occurrence of wildfires in Thailand, which every year result in forest fires and air pollution. Open burning has become the natural disaster that threatens wildlands and forest resources the most. Traditional firefighting systems, which are based on ground crew inspection, have several limits and dangerous risks. Aerial imagery technologies have become one of the most important tools to prevent wildfires, especially drone real-time monitoring for wildfire surveillance. This paper presents an accuracy assessment of drone real-time open burning imagery detection (Dr-TOBID) to detect smoke and burning as a framework for a deep learning-based object detection method using a combination of the YOLOv5 detector and a lightweight version of the long short-term memory (LSTM) classifier. The Dr-TOBID framework was designed using OpenCV, YOLOv5, TensorFlow, LebelImg, and Pycharm and wirelessly connected via live stream on open broadcaster software (OBS). The datasets were separated by 80% for training and 20% for testing. The resulting assessment considered the conditions of the drone’s altitudes, ranges, and red-green-black (RGB) mode in daytime and nighttime. The accuracy, precision, recall, and F1-Score are shown for the evaluation metrics. The quantitative results show that the accuracy of Dr-TOBID successfully detected open burning monitoring, smoke, and burning characteristics, where the average F1-score was 80.6% for smoke detection in the daytime, 82.5% for burning detection in the daytime, 77.9% for smoke detection at nighttime, and 81.9% for burning detection at nighttime.

AgriFireInfo v1.0: An Open-Source Platform for the Monitoring and Management of Open-Field Crop Residue Burning

“…In order to protect the environment and residents' health, numerous countries have implemented a series of policies and adopted heterogenous control measures to mitigate fire events [8,9]. These control measures primarily include traditional regular patrols [10,11], the installation of advanced electronic monitoring equipment [12], the utilization of unmanned aerial vehicles [13,14], and the establishment of watchtowers [15]. However, the above methods have inherent limitations, i.e., they are time-consuming and labor-intensive.…”

Forest Fire Image Deblurring Based on Spatial–Frequency Domain Fusion

“…Aerial imagery technology, particularly the use of UAVs equipped with optical cameras, has emerged as an important tool for wildfire prevention. These UAVs are capable of real-time monitoring and have gained popularity due to their versatility [5], high speed, and persistence. Their ability to integrate images from different flight altitudes enables wider coverage and the production of detailed images, making them the preferred choice for wildfire monitoring [6].…”