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

Yang, Huanyu; Wang, Jun; Wang, Jiacun

doi:10.3390/rs15235527

Cited by 10 publications

(4 citation statements)

References 64 publications

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“…The latter is further subdivided into hierarchical or partitional methods, offering a variety of algorithms to choose from [ 24 , 45 , 46 , 47 ]. In this study, we employed the K-means algorithm, which falls under the category of partitional algorithms [ 47 , 48 ]. K-means clustering partitions the data into distinct clusters based on the similarity of data points, providing a straightforward and efficient approach to clustering analysis.…”

Section: Methodsmentioning

confidence: 99%

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Soundscape Characterization Using Autoencoders and Unsupervised Learning

Nieto-Mora,

Ferreira de Oliveira,

Sanchez-Giraldo

et al. 2024

Sensors

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Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area’s soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.

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Section: Methodsmentioning

confidence: 99%

“…Moreover, previous work has shown that K-means is accurate and computationally efficient compared to others [ 47 , 49 ]. Additionally, several authors have succeeded in combining K-means clustering with Deep Neural Networks [ 48 ], suggesting it is a suitable choice for our purposes of exploring feature distributions in the data.…”

Section: Methodsmentioning

confidence: 99%

Soundscape Characterization Using Autoencoders and Unsupervised Learning

Nieto-Mora,

Ferreira de Oliveira,

Sanchez-Giraldo

et al. 2024

Sensors

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“…The reason why grids can generate prediction boxes is simple. Each grid has several (usually three) template prediction boxes, each with pre-defined width, height, coordinates, and confidence [8] . Confidence represents the probability of the presence of an object within the grid.…”

Section: Summary Of Yolov5mentioning

confidence: 99%

A Method of Lightweight Pedestrian Detection in Rainy and Snowy Weather Based on Improved YOLOv5

2024

AJCIS

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With the rapid development of artificial intelligence technology, pedestrian detection tasks have been widely used in intelligent security and other fields. In rainy and snowy weather, pedestrian detection models may be severely affected, resulting in missed detections and false alarms. This article proposes a lightweight pedestrian detection method based on improved YOLOv5 for rainy and snowy weather conditions. Firstly, to address the issue of insufficient datasets, artificial rain and snow noise is added to the collected pedestrian data. This increases the diversity of the data and improves the robustness of the model in handling pedestrian detection tasks in rainy and snowy weather. Next, a lightweight pedestrian detection model called MAC_YOLOv5s is designed based on the YOLOv5 network model. The MAC_YOLOv5s model replaces the backbone network of the original network with the Mobilenetv3 network to achieve light weight. In addition, the CBAM attention mechanism is integrated to enhance the model's anti-interference ability. The progressive feature pyramid network AFPN is introduced to fuse feature information between adjacent layers, extracting more important features. Experimental results show that the improved MAC_YOLOv5s lightweight pedestrian detection network model reduces the model size to one-third of the original YOLOv5s model, with a file size of only 5.35MB. The mAP value reaches 0.683. These results indicate that the improved MAC_YOLOv5s model has fewer parameters and a smaller model size compared to the original model, making it suitable for pedestrian detection tasks in rainy and snowy weather conditions.

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“…Current models for forest-fire-image detection rely on generating anchors or proposals for detection [33]. In this article, an innovative detection model named FSNet is proposed for detecting fire spots and smoke in forest-fire images.…”

Section: Forest Fire and Smoke Detectionmentioning

confidence: 99%

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

Wu,

Qian,

et al. 2024

Forests

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Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as Lforest, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model.

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

Cited by 10 publications

References 64 publications

Soundscape Characterization Using Autoencoders and Unsupervised Learning

Soundscape Characterization Using Autoencoders and Unsupervised Learning

A Method of Lightweight Pedestrian Detection in Rainy and Snowy Weather Based on Improved YOLOv5

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

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