FL-YOLOv7: A Lightweight Small Object Detection Algorithm in Forest Fire Detection

Xiao, Zhuo; Wan, Fang; Lei, Guangbo; Xiong, Ying; Xu, Li; Ye, Zhiwei; Liu, Wei; Zhou, Wen; Xu, Chengzhi

doi:10.3390/f14091812

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…Taking into account both precision and computational efficiency, we chose to integrate the Simam attention mechanism and WIoU loss function into YOLOv8, a decision supported by our comprehensive ablation study. This combination not only enhanced the detection accuracy in our dataset but also met the stringent real-time performance requirements of industrial applications [37]. The ablation study further corroborated the efficacy of both the Simam attention mechanism and WIoU loss function in our specific dataset.…”

Section: Comparative Experiments Of Attention Module and Loss Functionsupporting

confidence: 76%

Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors

Li,

Ye,

Zhou

et al. 2024

Applied Sciences

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In the domain of automatic visual inspection for miniature capacitor quality control, the task of accurately detecting defects presents a formidable challenge. This challenge stems primarily from the small size and limited sample availability of defective micro-capacitors, which leads to issues such as reduced detection accuracy and increased false-negative rates in existing inspection methods. To address these challenges, this paper proposes an innovative approach employing an enhanced ‘you only look once’ version 8 (YOLOv8) architecture specifically tailored for the intricate task of micro-capacitor defect inspection. The merging of the bidirectional feature pyramid network (BiFPN) architecture and the simplified attention module (SimAM), which greatly improves the model’s capacity to recognize fine features and feature representation, is at the heart of this methodology. Furthermore, the model’s capacity for generalization was significantly improved by the addition of the weighted intersection over union (WISE-IOU) loss function. A micro-capacitor surface defect (MCSD) dataset comprising 1358 images representing four distinct types of micro-capacitor defects was constructed. The experimental results showed that our approach achieved 95.8% effectiveness in the mean average precision (mAP) at a threshold of 0.5. This indicates a notable 9.5% enhancement over the original YOLOv8 architecture and underscores the effectiveness of our approach in the automatic visual inspection of miniature capacitors.

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Section: Comparative Experiments Of Attention Module and Loss Functionsupporting

confidence: 76%

Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors

Li,

Ye,

Zhou

et al. 2024

Applied Sciences

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“…Xiao et al [24] proposed the C3Ghost and GhostMP modules based on GhostNetv2 [25] to make the backbone of YOLOv7 lighter, thus reducing the computational cost and parameters of the model. In this paper, the standard convolution(SConv) of the Neck section is replaced by the lightweight GSConv [26].…”

Section: Lightweight Gsconvmentioning

confidence: 99%

FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

Yun,

Zheng,

Lin

et al. 2024

Fire

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Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detection accuracy of the machine learning forest fire detection method is limited, and it is unable to deal with complex scenes. Meanwhile, most deep learning methods are difficult to deploy due to high computational costs. To address these issues, this paper proposes a lightweight forest fire detection model based on YOLOv8 (FFYOLO). Firstly, in order to better extract the features of fire and smoke, a channel prior dilatation attention module (CPDA) is proposed. Secondly, the mixed-classification detection head (MCDH), a new detection head, is designed. Furthermore, MPDIoU is introduced to enhance the regression and classification accuracy of the model. Then, in the Neck section, a lightweight GSConv module is applied to reduce parameters while maintaining model accuracy. Finally, the knowledge distillation strategy is used during training stage to enhance the generalization ability of the model and reduce the false detection. Experimental outcomes demonstrate that, in comparison to the original model, FFYOLO realizes an mAP0.5 of 88.8% on a custom forest fire dataset, which is 3.4% better than the original model, with 25.3% lower parameters and 9.3% higher frames per second (FPS).

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“…While these approaches demonstrate promising outcomes in object detection, they have yet to integrate real-time capabilities with high accuracy in the realm of forest fire smoke detection. Xiao et al [55] introduced FL-YOLOv7, a lightweight model for small-target forest fire detection. By designing lightweight modules and incorporating Adaptive Spatial Feature Fusion (ASFF), they enhanced the model's capability to detect targets of various scales and its real-time performance.…”

Section: Deep Learning-based Approaches For Uav-based Smoke Detectionmentioning

confidence: 99%

Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning

Yang,

Wang,

Wang

2023

Remote Sensing

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Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention and damage mitigation. Combining deep learning techniques with UAV imagery holds potential in advancing forest fire smoke recognition. However, issues arise when using UAV-derived images, especially in detecting miniature smoke patches, complicating effective feature discernment. Common deep learning approaches for forest fire detection also grapple with limitations due to sparse datasets. To counter these challenges, we introduce a refined UAV-centric forest fire smoke detection approach utilizing YOLOv5. We first enhance anchor box clustering through K-means++ to boost the classification precision and then augment the YOLOv5 architecture by integrating a novel partial convolution (PConv) to trim down model parameters and elevate processing speed. A unique detection head is also incorporated to the model to better detect diminutive smoke traces. A coordinate attention module is embedded within YOLOv5, enabling precise smoke target location and fine-grained feature extraction amidst complex settings. Given the scarcity of forest fire smoke datasets, we employ transfer learning for model training. The experimental results demonstrate that our proposed method achieves 96% AP50 and 57.3% AP50:95 on a customized dataset, outperforming other state-of-the-art one-stage object detectors while maintaining real-time performance.

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FL-YOLOv7: A Lightweight Small Object Detection Algorithm in Forest Fire Detection

Cited by 13 publications

References 35 publications

Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors

Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors

FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning

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