论文标题
UTD-YOLOV5：一种实时水下目标检测方法，基于注意力的Yolov5
UTD-Yolov5: A Real-time Underwater Targets Detection Method based on Attention Improved YOLOv5
论文作者
论文摘要
作为自然宝藏，海洋拥有丰富的资源。但是对于海洋生物的可持续发展至关重要的珊瑚礁由于存在COT和其他生物而面临巨大的危机。通过体力劳动来保护社会的效率有限且效率低下。海洋环境的不可预测的本质也使手动操作冒险。在水下操作中使用机器人已成为一种趋势。但是，水下图像采集具有弱光，低分辨率和许多干扰等缺陷，而现有的目标检测算法无效。基于此，我们提出了一种基于注意力的Yolov5（称为UTD-Yolov5）的水下目标检测算法。它可以快速有效地检测COT，这又为复杂的水下操作提供了前提。我们在多个阶段调整了Yolov5的原始网络体系结构，包括：用两阶段的级联CSP（CSP2）代替原始骨干；引入视觉通道注意机制模块SE；设计随机锚点相似性计算方法等。这些操作使UTD-YOLOV5更灵活地检测并更准确地捕获功能。为了提高网络的效率，我们还提出了优化方法，例如WBF和迭代改进机制。本文根据CSIRO数据集进行了许多实验[1]。结果表明，我们的UTD-Yolov5的平均准确性达到78.54％，与基线相比，这是一个很大的提高。
As the treasure house of nature, the ocean contains abundant resources. But the coral reefs, which are crucial to the sustainable development of marine life, are facing a huge crisis because of the existence of COTS and other organisms. The protection of society through manual labor is limited and inefficient. The unpredictable nature of the marine environment also makes manual operations risky. The use of robots for underwater operations has become a trend. However, the underwater image acquisition has defects such as weak light, low resolution, and many interferences, while the existing target detection algorithms are not effective. Based on this, we propose an underwater target detection algorithm based on Attention Improved YOLOv5, called UTD-Yolov5. It can quickly and efficiently detect COTS, which in turn provides a prerequisite for complex underwater operations. We adjusted the original network architecture of YOLOv5 in multiple stages, including: replacing the original Backbone with a two-stage cascaded CSP (CSP2); introducing the visual channel attention mechanism module SE; designing random anchor box similarity calculation method etc. These operations enable UTD-Yolov5 to detect more flexibly and capture features more accurately. In order to make the network more efficient, we also propose optimization methods such as WBF and iterative refinement mechanism. This paper conducts a lot of experiments based on the CSIRO dataset [1]. The results show that the average accuracy of our UTD-Yolov5 reaches 78.54%, which is a great improvement compared to the baseline.
