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A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data
Target detection in offshore unmanned aerial vehicle data is still a challenge due to the complex characteristics of targets, such as multi-sizes, alterable orientation, and complex backgrounds. Herein, a YOLO-based detection model (YOLO-D) was proposed for target detection in offshore unmanned aerial vehicle data. Based on the YOLOv3 network, the residual module was improved by establishing dense connections and adding a dual-attention mechanism (CBAM) to enhance the use of features and global information. Then, the loss function of the YOLO-D model was added to the weight coefficients to increase detection accuracy for small-size targets. Finally, the feature pyramid network (FPN) was replaced by the secondary recursive feature pyramid network to reduce the impacts of a complicated environment. Taking the car, boat, and deposit near the coastline as the targets, the proposed YOLO-D model was compared against other models, including the faster R-CNN, SSD, YOLOv3, and YOLOv5, to evaluate its detection performance. The results showed that the evaluation metrics of the YOLO-D model, including precision (Pr), recall (Re), average precision (AP), and the mean of average precision (mAP), had the highest values. The mAP of the YOLO-D model increased by 37.95%, 39.44%, 28.46%, and 5.08% compared to the faster R-CNN, SSD, YOLOv3, and YOLOv5, respectively. The AP of the car, boat, and deposit reached 96.24%, 93.70%, and 96.79% respectively. Moreover, the YOLO-D model had a higher detection accuracy than other models, especially in the detection of small-size targets. Collectively, the proposed YOLO-D model is a suitable model for target detection in offshore unmanned aerial vehicle data.
A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data
Target detection in offshore unmanned aerial vehicle data is still a challenge due to the complex characteristics of targets, such as multi-sizes, alterable orientation, and complex backgrounds. Herein, a YOLO-based detection model (YOLO-D) was proposed for target detection in offshore unmanned aerial vehicle data. Based on the YOLOv3 network, the residual module was improved by establishing dense connections and adding a dual-attention mechanism (CBAM) to enhance the use of features and global information. Then, the loss function of the YOLO-D model was added to the weight coefficients to increase detection accuracy for small-size targets. Finally, the feature pyramid network (FPN) was replaced by the secondary recursive feature pyramid network to reduce the impacts of a complicated environment. Taking the car, boat, and deposit near the coastline as the targets, the proposed YOLO-D model was compared against other models, including the faster R-CNN, SSD, YOLOv3, and YOLOv5, to evaluate its detection performance. The results showed that the evaluation metrics of the YOLO-D model, including precision (Pr), recall (Re), average precision (AP), and the mean of average precision (mAP), had the highest values. The mAP of the YOLO-D model increased by 37.95%, 39.44%, 28.46%, and 5.08% compared to the faster R-CNN, SSD, YOLOv3, and YOLOv5, respectively. The AP of the car, boat, and deposit reached 96.24%, 93.70%, and 96.79% respectively. Moreover, the YOLO-D model had a higher detection accuracy than other models, especially in the detection of small-size targets. Collectively, the proposed YOLO-D model is a suitable model for target detection in offshore unmanned aerial vehicle data.
A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data
Zhenhua Wang (author) / Xinyue Zhang (author) / Jing Li (author) / Kuifeng Luan (author)
2021
Article (Journal)
Electronic Resource
Unknown
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