YOLOv5: Case Study of Image Resolution Influence on Fire Detection

Fire presents a dangerous occurrence in inhabited places or places occupied by humans. Apart from very high material damage, it can result in human fatalities. In the past, numerous sensor-based methods were developed in order to reduce the reaction time and improve fire detection accuracy. With the advent of modern convolutional neural networks (CNN), object detection models quickly emerged, together with novel fire detection methods utilizing said models. In this paper, we aim to demonstrate the performance of YOLOv5 on our custom dataset, which contains indoor fire occurrences. Furthermore, we are investigating the influence of the input image resolution on the models' performances. This is important due to the hardware limitations of the models that would be used in real-life applications. The research shows that different metrics (F1-Score, inference time, mAP50) can yield different models as the best-performing ones. However, since the models have to be relatively small, quick, and accurate, we have proposed the ranking-based evaluation of the models from the aspect of the input image resolution. The evaluation showed that the models obtained the best overall score when the input image resolution was set to
$512 \times 512$
pixels.