An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images

Abstract

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model’s capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model’s attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model’s effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model’s performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively.

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

“…The mean Average Precision (mAP) is computed by averaging the values of Average Precision (AP) calculated for each category [ 55 ]. Utilizing mAP as the primary metric allows for the identification of the model that attains the most superior overall performance in the specific task of detecting brain tumors.…”

Robust Forest Fire Detection Method for Surveillance Systems Based on You Only Look Once Version 8 and Transfer Learning Approaches

“…Furthermore, in [46], the authors propose ELASTIC-YOLOv3 as an enhancement over YOLOv2 to enhance performance without increasing the number of parameters. Initially, fire detection algorithms encountered challenges, such as high light intensity, limited color information, and variations in flame shapes and sizes, which prompted the development of enhanced technologies for real-time flame classification and recognition, manifested in modulated YOLO networks (v4, v5, v6, v7, v8) as introduced in [47][48][49][50][51][52][53].…”

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

“…YOLOv8 is composed of three parts: Backbone, FPN, and Yolo Head [23,24], as shown in Figure 2. The Backbone network uses CSPDarknet to extract the effective feature layer, and then realizes the feature fusion of the effective feature layer in FPN.…”