Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Harkat, Houda; Nascimento, Juarez Vieira do; Bernardino, Alexandre; Ahmed, Hasmath Farhana Thariq

doi:10.3390/rs14092023

Cited by 15 publications

(5 citation statements)

References 46 publications

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“…Introduced by Google researchers in 2018 [29], DeepLabv3+ is a state-of-the-art CNN model specifically designed for semantic segmentation tasks and represents the latest upgraded version of the DeepLab series models. It has achieved remarkable performances and found wide application in the domain of semantic segmentation [30][31][32][33]. As depicted in Figure 3, DeepLabv3+ comprises two primary components, an encoder and decoder [34].…”

Section: Deeplabv3+ Modelmentioning

confidence: 99%

An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry

Wang,

Wei,

Zhao

et al. 2024

Remote Sensing

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Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and mapping. Initially, the ResNet-50 network was employed for the binary classification of the cropped UASP orthophoto images. A comparative analysis was conducted to determine the optimal model between DeepLabv3+ and U-Net. Subsequently, the identified fissures were mosaicked and spatially projected onto the original orthophoto image, incorporating precise projection data, thereby furnishing a spatial reference for environmental governance. The results indicate a classification accuracy of 93% for the ResNet-50 model, with the U-Net model demonstrating a superior identification performance. Fissure orientation and distribution patterns are influenced by the mining direction, ground position of the mining workface, and topographic undulations. Enhancing the CNN performance can be achieved by incorporating variables such as slope indices, vegetation density, and mining workface locations. Lastly, a remote unmanned approach is proposed for the automated mapping of mining-induced fissures, integrated with UAS automated charging station technology. This study contributes to the advancement of intelligent, labor-saving, and unmanned management approaches advocated by the mining industry, with potential for broad applications in mining environmental protection efforts.

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Section: Deeplabv3+ Modelmentioning

confidence: 99%

An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry

Wang,

Wei,

Zhao

et al. 2024

Remote Sensing

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“…The selection of the loss function is a fundamental issue having a significant effect on CNNs' performances. Many studies [40], [41], [42], [43], [44] have examined the importance of loss functions for semantic segmentation. Especially in Jadon's study [41], different error functions are discussed.…”

Section: Loss Function Selectionmentioning

confidence: 99%

Ear semantic segmentation in natural images with Tversky loss function supported DeepLabv3+ convolutional neural network

Inan

Kacar

2022

Balkan Journal of Electrical and Computer Engineering

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Semantic segmentation is a fundamental problem for computer vision. On the other hand, for studies in the field of biometrics, semantic segmentation is gaining more importance. Many successful biometric recognition systems require a high- performance semantic segmentation algorithm. In this study, we present an effective ear segmentation technique in natural images. A convolutional neural network is trained for pixel-based ear segmentation. DeepLab v3+ network structure, with ResNet-18 as the backbone and Tversky lost function layer as the last layer, has been trained with natural and uncontrolled images. We perform the proposed network training using only the 750 images in the Annotated Web Ears (AWE) training set. The corresponding tests are performed on the AWE Test Set, University of Ljubljana Test Set, and the Collection A of In-The-Wild dataset. For the Annotated Web Ears (AWE) dataset, intersection over union (IoU) is measured as 86.3% for the AWE database. To the best of our knowledge, this is the highest performance achieved among the algorithms tested on the AWE test set.

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“…A semantic-segmentation-based method is a deep learning technique used to classify each pixel in the image according to object detection for forest fire identification. Semantic segmentation algorithms, such as Light-FireNet [25], DeepLabV3+ [26], and SegNet [27], are applications of deep learning that are not restricted to image classification and objection detection but can be used for semantic and instance segmentation.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance

Duangsuwan,

Klubsuwan

2023

Forests

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Open burning is the main factor contributing to the occurrence of wildfires in Thailand, which every year result in forest fires and air pollution. Open burning has become the natural disaster that threatens wildlands and forest resources the most. Traditional firefighting systems, which are based on ground crew inspection, have several limits and dangerous risks. Aerial imagery technologies have become one of the most important tools to prevent wildfires, especially drone real-time monitoring for wildfire surveillance. This paper presents an accuracy assessment of drone real-time open burning imagery detection (Dr-TOBID) to detect smoke and burning as a framework for a deep learning-based object detection method using a combination of the YOLOv5 detector and a lightweight version of the long short-term memory (LSTM) classifier. The Dr-TOBID framework was designed using OpenCV, YOLOv5, TensorFlow, LebelImg, and Pycharm and wirelessly connected via live stream on open broadcaster software (OBS). The datasets were separated by 80% for training and 20% for testing. The resulting assessment considered the conditions of the drone’s altitudes, ranges, and red-green-black (RGB) mode in daytime and nighttime. The accuracy, precision, recall, and F1-Score are shown for the evaluation metrics. The quantitative results show that the accuracy of Dr-TOBID successfully detected open burning monitoring, smoke, and burning characteristics, where the average F1-score was 80.6% for smoke detection in the daytime, 82.5% for burning detection in the daytime, 77.9% for smoke detection at nighttime, and 81.9% for burning detection at nighttime.

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Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Cited by 15 publications

References 46 publications

An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry

An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry

Ear semantic segmentation in natural images with Tversky loss function supported DeepLabv3+ convolutional neural network

Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance

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