Enhancing Multi-Category Smoke Detection Using Similarity Constrained Loss

Smoke detection plays a crucial role in ensuring public safety across various domains, including industrial settings, daily life, and disaster management. The effectiveness of smoke detection models heavily relies on the availability of comprehensive datasets and the optimization of loss functions. However, existing smoke detection research primarily focuses on fire-related scenarios, overlooking the significant differences in characteristics between smoke generated by different causes. To address this issue, we have developed a Multi-Category smoke detection dataset MC-smoke dataset , which is organized based on the smoke's origin and main components. This dataset includes three categories of smoke and contains a total of 1,115 images. Furthermore, to alleviate the loss ambiguity issue present in existing object detection losses, we propose a novel Similarity-Constrained SC loss function. This function uses a similarity constraint coefficient in the bounding box to influence center regression and vertex regression losses, enabling more accurate smoke detection. Lastly, extensive experiments were conducted on both the MC-smoke dataset and the classic object detection dataset PASCAL VOC 2007, validating the substantial effectiveness enhancement achieved by the SC loss function. Additionally, comprehensive baseline and comparative experiments were conducted to affirm the suitability of the MC-smoke dataset for research about smoke detection training, testing, and validation.