Affiliations: College of Software, Nankai University, Tianjin, China
Correspondence:
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Corresponding author. Tiegang Gao, College of Software, Nankai University, Tianjin, 300457, China. E-mail: gaotiegang@nankai.edu.cn.
Abstract: Unmanned aerial vehicles (UAVs) play a crucial role in maritime search and rescue missions, capturing images of open water scenarios and assisting in object detection. Previous object detection models have mainly focused on general scenarios. However, existing object detection models have mainly focused on general scenarios, while images captured by UAVs in vast ocean scenarios often contain numerous small objects that significantly degrade the performance of the original models. To address this challenge, we propose a model that can automatically detect objects in images captured by UAVs during maritime search and rescue missions. Our approach involves designing a new detection head with higher resolution feature maps and more comprehensive feature information to improve the detection of small objects. Additionally, we integrate Swin Transformer blocks into the small object detection head, which can improve the model’s ability to obtain abundant contextual information and thus improves the model’s ability to detect small objects. Moreover, we fuse the Convolutional Block Attention Model into the small object detection head to help the model focus on important features. Finally, we adopt a model ensemble strategy to further improve the mean average precision (mAP). Our proposed model achieves a 4.05% improvement in mAP compared to the baseline model. Furthermore, our model outperforms the previous state-of-the-art model on the SeaDronesSee dataset in terms of fewer parameters, lower training costs, and higher mAP.
Keywords: Deep learning, object detection, YOLOv5, Swin Transformer, UAV
