Early fire detection is crucial for reducing losses due to fire. Therefore, the development of an efficient fire detection algorithm and alarm system is essential. The image fire detection algorithm is based on the mathematical analysis of images. Current algorithm evaluation methods are not effective and cannot sufficiently distinguish the performance of different detection algorithms. Therefore, such methods are not conducive for evaluating and improving algorithms. Based on a large-scale fire image dataset, the ground-truth complexity of images was quantified according to the time required for humans to detect the presence or absence of fire in the images. Four image complexity metrics based on the characteristics of fire detection are proposed. A comparison of the ground-truth and predicted scores of image complexity revealed that the comprehensive image complexity metric based on the Inception Resnet -v2 predictor was the most effective measurement. Its predicted scores ranked approximately 85% image pairs in the same order as that of the ground-truth complexity scores. Finally, a novel method for evaluating the performance of an image fire detection algorithm based on image complexity is proposed. Evaluation of the performance of five algorithms revealed that the performance of algorithm differs considerably and the proposed method can accurately determine the detection level of the detection algorithm in different image complexity conditions. The results of the study provide a valuable reference for developing and optimizing detection algorithms.

早期火灾探测对于减少火灾损失至关重要。因此，开发有效的火灾探测算法和报警系统至关重要。图像火灾检测算法基于图像的数学分析。当前的算法评估方法无效，不能充分区分不同检测算法的性能。因此，这种方法不利于评估和改进算法。基于大规模火灾图像数据集，根据人类检测图像中火源存在或不存在所需的时间来量化图像的真实性。提出了四种基于火灾探测特征的图像复杂度指标。通过对图像复杂度的真实性和预测分数进行比较，发现基于Inception Resnet -v2预测器的综合图像复杂度度量标准是最有效的度量。它的预测分数与真实度复杂度分数的顺序相同，对大约85％的图像对进行排名。最后，提出了一种基于图像复杂度的图像火灾检测算法性能评估的新方法。对五种算法性能的评估表明，该算法的性能差异很大，所提方法可以准确确定不同图像复杂度条件下检测算法的检测水平。研究结果为开发和优化检测算法提供了有价值的参考。