Abstract
Object detection on the water surface is crucial for unmanned surface vehicles in maritime environments. Despite the challenges posed by variable lighting and ocean conditions, advancements in this field are necessary. In this paper, we investigate the transferability of YOLOv5-based water surface object detection models in cross-domain scenarios. The evaluation is based on publicly available datasets and two newly proposed datasets, Taihu Trial Dataset(TTD) and Fuxian Trial Dataset(FTD), which contain similar target classes but distinct scene and features. Results from extensive experiments indicate that zero-shot transfer is challenging, but a limited number of samples from the target domain can greatly enhance model performance.
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Guo, Y., Chen, Z., Wang, Q., Bao, T., Zhou, Z. (2023). Investigating the Transferability of YOLOv5-Based Water Surface Object Detection Model in Maritime Applications. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1870. Springer, Singapore. https://doi.org/10.1007/978-981-99-5847-4_8
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