# Multi-Sensor Photoelectric Fire Alarm Device Implementation for Early Fire Detection in Campsites
## 2 Related Studies  
Currently, smoke detectors are categorized into two main types: visual and non-visual detectors. Recent studies have primarily focused on visual detectors, with efforts aimed at reducing error rates and enhancing detection accuracy. For example, [PERSON] [15] proposed a visual smoke detection algorithm that minimizes false detections by employing image preprocessing techniques, including HSV color transformation and Harris Corner Detection, to minimize the miss detection rate. In another notable study, [PERSON] [16] developed a fire and smoke detection system using video cameras, employing a deep learning model called YOLOv2 to reduce the computational load of complex models and decrease the false alarm rate. However, visual fire detectors present certain challenges despite their advanced capabilities. These systems require dedicated video feeds and extensive knowledge of the monitored areas, coupled with high installation costs. Given these limitations, non-visual methods play a crucial role in providing cost-effective and widely accessible fire detection solutions. The most widely used non-visual smoke detector is the photoelectric detector. This type of detector operates by detecting light scattering caused by smoke particles using a light-emitting element and a photodetector. When smoke is present, light scattering reduces the amount of light reaching the photodetector, indicating a fire. The widespread adoption of photoelectric smoke detectors is largely due to their relatively simple design and affordability compared to other types. However, these detectors are prone to higher error rates and slower response times, which has led to ongoing research efforts aimed at improving their performance. Recent advances in non-visual detection methods are exemplified by the work of [PERSON], [PERSON] [17], who proposed a method for detecting smoldering fires using a capacitive MEMS hydrogen sensor equipped with a microheater, enabling fire detection with minimal smoke production. This structure successfully detected 66-ppm hydrogen generated by fire. In addition, [PERSON], [PERSON] [18] proposed a method for detecting fires using a carbon monoxide (CO) sensor for early fire detection. The system was designed to detect CO with high sensitivity by applying wavelength modulation spectroscopy (WMS) to a 32-bit microcontroller. However, these non-visual detectors do not lack challenges. Despite their effectiveness in detecting hydrogen or CO, they do not significantly improve the error rate. As highlighted by [PERSON] [19], many detection technologies are available for fire detection; however, no single solution is universally applicable. This underscores the need for an ongoing discussion and exploration of various methods tailored to different situations and locations. In contrast, a significant body of research on fire occurrences has leveraged time-series meteorological data, including variables such as temperature, humidity, wind speed, cloud cover, atmospheric pressure, and precipitation, along with wildfire occurrence data. These datasets are often used in logistic regression models to estimate wildfire probabilities. For instance, [PERSON] [20] recently identified a correlation between increasing regional wildfires and climate change, analyzing the trends between each factor, and highlighting the impact of decreasing relative humidity and rising temperatures. Similarly, to quantitatively assess the causal relationship between meteorological factors and wildfires, [PERSON] [21] investigated the factors influencing seasonal wildfires in Gangwon Province, South Korea,using PLS-SEM. In addition, to forecast the risk of large wildfire occurrences, [PERSON] [22] developed criteria for predicting the risk of large wildfires through a case analysis spanning approximately 20 years using effective humidity and average wind speed.