Due to its wide monitoring range and low cost, visual-based fire detection technology is commonly used for fire detection in open spaces. However, traditional fire detection algorithms have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. These algorithms have poor anti-interference ability against fire-like objects, such as emissions from factory chimneys, clouds, etc. In this study, we developed a fire detection approach based on an improved YOLOv5 algorithm and a fire detection dataset with fire-like objects. We added three Convolutional Block Attention Modules (CBAMs) to the head network of YOLOv5 to improve its feature extraction ability. Meanwhile, we used the C2f module to replace the original C2 module to capture rich gradient flow information. Our experimental results show that the proposed algorithm achieved a mAP@50 of 82.36% for fire detection. In addition, we also conducted a comparison test between datasets with and without labeling information for fire-like objects. Our results show that labeling information significantly reduced the false-positive detection proportion of fire-like objects incorrectly detected as fire objects. Our experimental results show that the CBAM and C2f modules enhanced the network’s feature extraction ability to differentiate fire objects from fire-like objects. Hence, our approach has the potential to improve fire detection accuracy, reduce false alarms, and be more cost-effective than traditional fire detection methods. This method can be applied to camera monitoring systems for automatic fire detection with resistance to fire-like objects.

Mass concentration is a commonly used but insufficient metric to evaluate the particulate matter (PM) exposure hazard. Recent studies have declared that small particles have more serious impacts on human health than big particles given the same mass concentration. However, state-of-the-art PM sensors cannot provide explicit information of the particle size for further analysis. In this work, we adopt Sauter mean diameter (SMD) as a key metric to reflect the particle size besides the mass concentration. To measure SMD, an effective optical sensing method and a proof-of-concept prototype sensor are proposed by using dual wavelengths technology. In the proposed method, a non-linear conversion model is developed to improve the SMD measurement accuracy for aerosol samples of different particle size distributions and reflective indices based on multiple scattering channels. In the experiment of Di-Ethyl-Hexyl-Sebacate (DEHS) aerosols, the outputs of our prototype sensor demonstrated a good agreement with existing laboratory reference instruments with maximum SMD measurement error down to 7.04%. Furthermore, the simplicity, feasibility and low-cost features of this new method present great potential for distributed PM monitoring, to support sophisticated human exposure hazard assessment.

The concentration of lung deposition surface area (LDSA) is a crucial metric for assessing potential health impacts associated with particle size distribution. Traditional LDSA sensors, which rely on particle charging, are limited to measuring particles up to 300 nm in size. However, aerosols in the submicron to micron range are also significant for evaluating exposure risks. In this study, we introduce an optical method for LDSA sensing that leverages light-scattering angular spectrum and deep learning techniques. Our approach utilizes a compact, lens-free sensor to sample angular spectrum signals, which are then processed by a ResNet-like neural network to adjust the conversion factor between LDSA concentration and scattering light intensity. Experimental results demonstrate that our prototype achieves a maximum relative error of 14.78% in measuring the angular spectrum. Additionally, the neural network compensates for variations in particle size distribution and refractive index, refining the conversion factor. When compared to reference LDSA concentrations provided by laboratory instruments, our prototype maintains relative errors within 17.66%. With its simple mechanical design, high accuracy, and cost-effectiveness, this method presents a promising solution for online and field measurements of LDSA concentration.