  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.
