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Fire detection is highly important for people's lives and property, and enhancing its accuracy is essential. This study focused on utilizing and improving YOLOv8 to obtain higher detection accuracy for fire detection. Three methods were used. First, the newly designed DGIConv module replaces the original Conv module, thereby decreasing the computational complexity while enhancing the model's performance. Second, to enhance the recognition ability of flame targets, a new attention mechanism named FourBranchAttention was designed, and a comparison was made with other attention mechanisms. The experiments revealed that the newly designed attention mechanism performed best on the mAP50 and mAP50-95 metrics. Finally, to improve the convergence speed and localization ability of the model, the loss function is optimized by adopting better hyperparameters of the TaskAlignedAssigner and employing the newly designed GSIoU as an alternative to the original CIoU. Through ablation experiments, all three improvements improved the detection performance to a certain extent, and the model using the three improvements achieved the best performance. Compared with the baseline, the YOLOv8 model with DGIConv, FourBranchAttention, and the optimized loss function increased the mAP50 by 2.52% and the mAP50-95 by 3.37%. The mAP50 and mAP50-95 had reached 98.46% and 75.26%, respectively. Compared with previous models, such as SSD/YOLOv7, the performance metrics of enhanced YOLOv8 also exhibited significant enhancements, thereby augmenting the accuracy of fire detection. Doi: 10.28991/HIJ-2024-05-03-09 Full Text: PDF |
