Development of a mid-infrared sensor system for early fire identification in cotton harvesting operations

Li, Yafei; Lu, Yang; Zheng, Chuantao; Yang, Shuo; Zheng, Kaiyuan; Song, Fang; Li, Chunguang; Ye, Weilin; Zhang, Yu; Wang, Yiding; Tittel, Frank K.

doi:10.1039/d2an01523d

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Traditional fire detection technology relies mainly on sensors, of which temperature and smoke sensors are the most widely used sensors, which convert temperature and smoke information into electrical signals with high accuracy and sensitivity 1 . With the development of sensor technology, infrared fire sensors can detect the occurrence of a fire by taking advantage of the fact that the burning of a substance produces a specific spectrum of radiation as it is not dependent on the temperature or smoke produced by the fire 2 . However, this method also has the disadvantage of being susceptible to interference from other sources of radiation.…”

Section: Introductionmentioning

confidence: 99%

A lightweight fire detection algorithm for small targets based on YOLOv5s

Lv,

Zhou,

Chen

et al. 2024

Sci Rep

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In response to the current challenges fire detection algorithms encounter, including low detection accuracy and limited recognition rates for small fire targets in complex environments, we present a lightweight fire detection algorithm based on an improved YOLOv5s. The introduction of the CoT (Contextual Transformer) structure into the backbone neural network, along with the creation of the novel CSP1_CoT (Cross stage partial 1_contextual transformer) module, has effectively reduced the model’s parameter count while simultaneously enhancing the feature extraction and fusion capabilities of the backbone network; The network’s Neck architecture has been extended by introducing a dedicated detection layer tailored for small targets and incorporating the SE (Squeeze-and-Excitation) attention mechanism. This augmentation, while minimizing parameter proliferation, has significantly bolstered the interaction of multi-feature information, resulting in an enhanced small target detection capability; The substitution of the original loss function with the Focal-EIoU (Focal-Efficient IoU) loss function has yielded a further improvement in the model’s convergence speed and precision; The experimental results indicate that the modified model achieves an mAP@.5 of 96% and an accuracy of 94.8%, marking improvements of 8.8% and 8.9%, respectively, over the original model. Furthermore, the model’s parameter count has been reduced by 1.1%, resulting in a compact model size of only 14.6MB. Additionally, the detection speed has reached 85 FPS (Frames Per Second), thus satisfying real-time detection requirements. This enhancement in precision and accuracy, while simultaneously meeting real-time and lightweight constraints, effectively caters to the demands of fire detection.

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Section: Introductionmentioning

confidence: 99%

A lightweight fire detection algorithm for small targets based on YOLOv5s

Lv,

Zhou,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Given the escalating societal demands for applications encompassing environmental monitoring, medical diagnostics, and industrial production, the imperative to the development of sensors characterized by high performance, sensitivity, and selectivity has never been more pressing. Among the array of sensor technologies, gas sensors find widespread applications in vital sectors such as environmental monitoring, − chemical analysis, , and deep-sea exploration. , Nevertheless, traditional infrared gas sensors relying on optical fibers and free-space lasers, while capable of achieving low detection limits, grapple with inherent drawbacks such as an unwieldy dimension and intricate alignment requirements. − The imminent generation of gas sensors is poised to be compact, portable, cost-effective, and endowed with real-time trace gas detection capabilities. In response to this demand, the evolution of nanomanufacturing technology has propelled nanophotonic waveguide sensors into the spotlight as prospective solutions for surmounting these challenges.…”

mentioning

confidence: 99%

Slow-Light-Enhanced Polarization Division Multiplexing Infrared Absorption Spectroscopy for On-Chip Wideband Multigas Detection in a 1D Photonic Crystal Waveguide

Peng,

Huang,

Zheng

et al. 2024

Anal. Chem.

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Slow-light photonic crystal waveguide (PCW) gas sensors based on infrared absorption spectroscopy play a pivotal role in enhancing the on-chip interaction between light and gas molecules, thereby significantly boosting sensor sensitivity. However, two-dimensional (2D) PCWs are limited by their narrow mode bandwidth and susceptibility to polarization, which restricts their ability for multigas measurement. Due to quasi-TE and quasi-TM mode guiding characteristics in one-dimensional (1D) PCW, a novel slow-light-enhanced polarization division multiplexing infrared absorption spectroscopy was proposed for on-chip wideband multigas detection. The optimized 1D PCW gas sensor experimentally shows an impressive slow-light mode bandwidth exceeding 100 nm (TM, 1500–1550 nm; TE, 1610–1660 nm) with a group index ranging from 4 to 25 for the two polarizations. The achieved bandwidth in the 1D PCW is 2–3 times that of the reported quasi-TE polarized 2D PCWs. By targeting the absorption lines of different gas species, multigas detection can be realized by modulating the lasers and demodulating the absorption signals at different frequencies. As an example, we performed dual-gas measurements with the 1D PCW sensor operating in TE mode at 1.65 μm for methane (CH4) detection and in TM mode at 1.53 μm for acetylene (C2H2) detection. The 1 mm long sensor achieved a remarkable limit of detection (LoD) of 0.055% for CH4 with an averaging time of 17.6 s, while for C2H2, the LoD was 0.18%. This polarization multiplexing sensor shows great potential for on-chip gas measurement because of the slow-light enhancement in the light–gas interaction effect as well as the large slow-light bandwidth for multigas detection.

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Development and Field Deployment of a ppb-Level SO₂/NO₂ Dual-Gas Sensor System for Agricultural Early Fire Identification

Guan,

Wu,

Liu

et al. 2024

ACS Sens.

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Development of a mid-infrared sensor system for early fire identification in cotton harvesting operations

Abstract: To realize early fire identification in cotton harvesting operation, a mid-infrared carbon monoxide (CO) sensor system was developed. To match the broadband light source with a 15° divergence angle, a...

Cited by 3 publications

References 34 publications

A lightweight fire detection algorithm for small targets based on YOLOv5s

A lightweight fire detection algorithm for small targets based on YOLOv5s

Slow-Light-Enhanced Polarization Division Multiplexing Infrared Absorption Spectroscopy for On-Chip Wideband Multigas Detection in a 1D Photonic Crystal Waveguide

Development and Field Deployment of a ppb-Level SO₂/NO₂ Dual-Gas Sensor System for Agricultural Early Fire Identification

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Development of a mid-infrared sensor system for early fire identification in cotton harvesting operations

Abstract: To realize early fire identification in cotton harvesting operation, a mid-infrared carbon monoxide (CO) sensor system was developed. To match the broadband light source with a 15° divergence angle, a...

Cited by 3 publications

References 34 publications

A lightweight fire detection algorithm for small targets based on YOLOv5s

A lightweight fire detection algorithm for small targets based on YOLOv5s

Slow-Light-Enhanced Polarization Division Multiplexing Infrared Absorption Spectroscopy for On-Chip Wideband Multigas Detection in a 1D Photonic Crystal Waveguide

Development and Field Deployment of a ppb-Level SO2/NO2 Dual-Gas Sensor System for Agricultural Early Fire Identification

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Development and Field Deployment of a ppb-Level SO₂/NO₂ Dual-Gas Sensor System for Agricultural Early Fire Identification