A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data

Zhenhua Wang, Xinyue Zhang, Jing Li and Kuifeng Luan
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Zhenhua Wang: College of Information Technology, Shanghai Ocean University, Shanghai 203106, China
Xinyue Zhang: College of Information Technology, Shanghai Ocean University, Shanghai 203106, China
Jing Li: College of Information Technology, Shanghai Ocean University, Shanghai 203106, China
Kuifeng Luan: College of Marine Sciences, Shanghai Ocean University, Shanghai 203106, China

Sustainability, 2021, vol. 13, issue 23, 1-11

Abstract: 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.

Keywords: offshore monitoring; target detection; deep learning; YOLO; unmanned aerial vehicle (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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