Analysis and Design of a Yolo like DNN for Smoke/Fire Detection for Low-cost Embedded Systems

Gagliardi, Alessio; Villella, Marco; Picciolini, Luca; Saponara, Sergio

doi:10.1007/978-3-030-66729-0_2

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Zhao, Lei et al [ 30 ] proposed fire-YOLO model to detect small targets based on YOLO. Gagliardi et al [ 31 ] proposed a faster region-based convolutional neural network (R-CNN) to detect suspicious fire regions (SRoF) and non-fire regions based on their spatial features. Abdusalomov et al [ 32 ], combined with sensors, proposed a real-time high speed fire detection model based on YOLO.…”

Section: Related Workmentioning

confidence: 99%

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Mei

et al. 2022

Sensors

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Ship fire is one of the greatest dangers to ship navigation safety. Nevertheless, typical detection methods have limited detection effectiveness and accuracy due to distance restrictions and ship motion. Although the issue can be addressed by image recognition algorithms based on deep learning, the computational complexity and efficiency for ship detection are tough. This paper proposes a lightweight target identification technique based on the modified YOLOv4-tiny algorithm for the precise and efficient detection of ship fires, taking into account the distinctive characteristics of ship fires and the marine environment. Initially, a multi-scale detection technique is applied to broaden the detection range and integrate deep semantic information, thereby enhancing the feature information of small targets and obscured objects and improving the detection precision. Then, the proposed algorithm employs the SE attention mechanism for inter-channel feature fusion to improve the capability of feature extraction and the precision of ship fire detection. Last but not least, picture transformation and migration learning are added to the small ship fire dataset to accelerate the convergence pace, improve the convergence effect, and reduce dataset dependence. The simulation experiments reveal that the proposed I-YOLOv4-tiny + SE model outperforms the benchmark algorithm in terms of ship fire detection accuracy and detection efficiency and that it satisfies the real-time ship fire warning criteria in demanding maritime environments.

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Section: Related Workmentioning

confidence: 99%

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Mei

et al. 2022

Sensors

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“…However, there are still some small targets missed. The proposed method [9] uses a Faster Region-Based Convolutional Neural Network (R-CNN) to detect suspicious fire regions (SRoF) and nonfire regions based on their spatial features. This can successfully improve fire detection accuracy by reducing false detections, yet the detection speed is relatively slow.…”

Section: Introductionmentioning

confidence: 99%

Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

et al. 2022

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For the detection of small targets, fire-like and smoke-like targets in forest fire images, as well as fire detection under different natural lights, an improved Fire-YOLO deep learning algorithm is proposed. The Fire-YOLO detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets identification, improves network performance, and reduces model parameters. Furthermore, through the promotion of the feature pyramid, the top-performing prediction box is obtained. Fire-YOLO attains excellent results compared to state-of-the-art object detection networks, notably in the detection of small targets of fire and smoke. Overall, the Fire-YOLO detection model can effectively deal with the inspection of small fire targets, as well as fire-like and smoke-like objects. When the input image size is 416 × 416 resolution, the average detection time is 0.04 s per frame, which can provide real-time forest fire detection. Moreover, the algorithm proposed in this paper can also be applied to small target detection under other complicated situations.

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An efficient model for real-time wildfire detection in complex scenarios based on multi-head attention mechanism

Wang

Pan

Gao

et al. 2023

J Real-Time Image Proc

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Analysis and Design of a Yolo like DNN for Smoke/Fire Detection for Low-cost Embedded Systems

Cited by 3 publications

References 15 publications

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

An efficient model for real-time wildfire detection in complex scenarios based on multi-head attention mechanism

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