15 research outputs found
Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance
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Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning
No full textIn this paper, we propose an object detection methodology applied to Global Wheat Head Detection (GWHD) Dataset. We have been through two major architectures of object detection which are Faster R-CNN, and EfficientDet, in order to design a novel and robust wheat head detection model. We emphasize on optimizing the performance of our proposed final architectures. Furthermore, we have been through an extensive exploratory data analysis, data cleaning, data splitting and adapted best data augmentation techniques to our context. We use semi supervised learning, precisely pseudo-labeling, to boost previous supervised models of object detection. Moreover, we put much effort on ensemble learning including test time augmentation, multi-scale ensemble and bootstrap aggregating to achieve higher performance. Finally, we use weighted boxes fusion as our post processing technique to optimize our wheat head detection results. Our solution has been submitted to solve a research challenge launched on the GWHD Dataset which was led by nine research institutes from seven countries. Our proposed method was ranked within the top 6% in the above-mentioned challenge
Loop Closure Detection for Monocular Visual Odometry: Deep-Learning Approaches Comparison
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Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation
No full textWildfires are a worldwide natural disaster causing important economic damages and loss of lives. Experts predict that wildfires will increase in the coming years mainly due to climate change. Early detection and prediction of fire spread can help reduce affected areas and improve firefighting. Numerous systems were developed to detect fire. Recently, Unmanned Aerial Vehicles were employed to tackle this problem due to their high flexibility, their low-cost, and their ability to cover wide areas during the day or night. However, they are still limited by challenging problems such as small fire size, background complexity, and image degradation. To deal with the aforementioned limitations, we adapted and optimized Deep Learning methods to detect wildfire at an early stage. A novel deep ensemble learning method, which combines EfficientNet-B5 and DenseNet-201 models, is proposed to identify and classify wildfire using aerial images. In addition, two vision transformers (TransUNet and TransFire) and a deep convolutional model (EfficientSeg) were employed to segment wildfire regions and determine the precise fire regions. The obtained results are promising and show the efficiency of using Deep Learning and vision transformers for wildfire classification and segmentation. The proposed model for wildfire classification obtained an accuracy of 85.12% and outperformed many state-of-the-art works. It proved its ability in classifying wildfire even small fire areas. The best semantic segmentation models achieved an F1-score of 99.9% for TransUNet architecture and 99.82% for TransFire architecture superior to recent published models. More specifically, we demonstrated the ability of these models to extract the finer details of wildfire using aerial images. They can further overcome current model limitations, such as background complexity and small wildfire areas
