Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance andmanagement.However,challenges like small object detection,scale variation,and the presen...Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance andmanagement.However,challenges like small object detection,scale variation,and the presence of closely packed objects in these images hinder accurate detection.Additionally,the motion blur effect further complicates the identification of such objects.To address these issues,we propose enhanced YOLOv9 with a transformer head(YOLOv9-TH).The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms.We further improve YOLOv9-TH using several strategies,including data augmentation,multi-scale testing,multi-model integration,and the introduction of an additional classifier.The cross-stage partial(CSP)method and the ghost convolution hierarchical graph(GCHG)are combined to improve detection accuracy by better utilizing feature maps,widening the receptive field,and precisely extracting multi-scale objects.Additionally,we incorporate the E-SimAM attention mechanism to address low-resolution feature loss.Extensive experiments on the VisDrone2021 and DIOR datasets demonstrate the effectiveness of YOLOv9-TH,showing good improvement in mAP compared to the best existing methods.The YOLOv9-TH-e achieved 54.2% of mAP50 on the VisDrone2021 dataset and 92.3% of mAP on the DIOR dataset.The results confirmthemodel’s robustness and suitability for real-world applications,particularly for small object detection in remote sensing images.展开更多
Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi Arabia.Makkah is one of the most important cities in Saudi Arabia that needs to be protected from des...Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi Arabia.Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification.The vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate change.The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area.In this paper,we propose an automatic desertification detection system based on Deep Learning techniques.Aerial images are classified using Convolutional Neural Networks(CNN)to detect land state variation in real-time.CNNs have been widely used for computer vision applications,such as image classification,image segmentation,and quality enhancement.The proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded implementation.It has achieved high efficiency with 96.47% accuracy.In light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.展开更多
文摘Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance andmanagement.However,challenges like small object detection,scale variation,and the presence of closely packed objects in these images hinder accurate detection.Additionally,the motion blur effect further complicates the identification of such objects.To address these issues,we propose enhanced YOLOv9 with a transformer head(YOLOv9-TH).The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms.We further improve YOLOv9-TH using several strategies,including data augmentation,multi-scale testing,multi-model integration,and the introduction of an additional classifier.The cross-stage partial(CSP)method and the ghost convolution hierarchical graph(GCHG)are combined to improve detection accuracy by better utilizing feature maps,widening the receptive field,and precisely extracting multi-scale objects.Additionally,we incorporate the E-SimAM attention mechanism to address low-resolution feature loss.Extensive experiments on the VisDrone2021 and DIOR datasets demonstrate the effectiveness of YOLOv9-TH,showing good improvement in mAP compared to the best existing methods.The YOLOv9-TH-e achieved 54.2% of mAP50 on the VisDrone2021 dataset and 92.3% of mAP on the DIOR dataset.The results confirmthemodel’s robustness and suitability for real-world applications,particularly for small object detection in remote sensing images.
基金by Makkah Digital Gate Initiative under grant no.(MDP-IRI-3-2020).
文摘Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi Arabia.Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification.The vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate change.The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area.In this paper,we propose an automatic desertification detection system based on Deep Learning techniques.Aerial images are classified using Convolutional Neural Networks(CNN)to detect land state variation in real-time.CNNs have been widely used for computer vision applications,such as image classification,image segmentation,and quality enhancement.The proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded implementation.It has achieved high efficiency with 96.47% accuracy.In light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.
