基于改进Mobilenet-YOLOv3的轻量级水下生物检测算法

在水下生物检测中，经典目标检测模型由于体积大、参数量多，不适用于微小型水下硬件设备，而现有轻量化模型又难以平衡检测精度和实时性.针对这一问题，本研究提出了基于改进Mobilenet-YOLOv3的轻量级检测算法CPM-YOLOv3，该算法利用规整通道剪枝算法对Mobilenet-YOLOv3进行剪枝，并将特征提取网络中的SE(squeeze-and-excitation)模块替换成CBAM (convolutional block attention module)，实现对网络模型的压缩.同时，在不同尺寸的检测层中分别加入2个CBAM，在几乎不增加模型大小的情况下提升模型关注目标特征信息的能力.实验结果表明，CPM-YOLOv3模型大小仅有4.86 MB，与原模型相比大小降低了94.7%，平均检测精度为87.0%，速度为5.1 ms/帧.相较于其他网络模型，CPM-YOLOv3更适合在微小型水下设备中应用.

Lightweight underwater biological detection algorithm based on improved Mobilenet-YOLOv3

In underwater biological detection, the classical target detection model is not suitable for small underwater hardware equipment due to its large volume and large number of parameters, and the existing lightweight model is difficult to balance detection accuracy and real-time performance. To solve this problem, a lightweight detection algorithm CPM-YOLOv3 was proposed based on the improved Mobilenet-YOLOv3. The regular channel pruning algorithm was used to pruning Mobilenet-YOLOv3, and the squeeze-and-excitation (SE) module in the feature extraction network was replaced with convolutional block attention module (CBAM) to compress the network model. At the same time, two CBAM were added to the detection layer of different sizes to improve the model's ability to pay attention to target feature information without increasing the size of the model. Experimental results showed that the size of CPM-YOLOv3 model was only 4.86 MB, which was reduced by 94.7% compared with the original model. The average detection precision was 87.0%, and the speed was 5.1 ms/frame. Compared with other network models, CPM-YOLOV3 is more suitable for the application of micro underwater equipment.