针对水面无人艇在复杂海况下的局部避障问题，文中引入深度学习方法来处理 视觉信息，提出了结合 VFH +算法的水面无人艇的局部路径规划方法． 首先利用对称的 编码器 －解码器结构的图像语义分割模型和 Faster RCNN 网络模型进行水面边界线检测 及水面障碍物识别，构建水面无人艇环境模型; 然后采用基于 VFH + 的局部路径规划方 法，通过逐步构建主直方图、二元直方图和掩模直方图压缩环境数据，引入合理的代价函 数来获取实现水面无人艇的有效避障方向规划． 在 MODD 图像数据集上的仿真实验以及 实船避障实验结果表明，该方法能有效地提取水面图像信息，并得到合理的局部路径规划 策略，在10kn 航速下的避障轨迹平滑，可满足水面无人艇的自主避障需求． 

Aiming to solve the problem of local obstacle avoidance for unmanned surface vehicle under complex marine conditions，deep learning was introduced to deal with the vision information and completed the local path planning for unmanned surface vehicle combining with VFH + algorithm． The image semantic segmentation model with symmetrical encoder-decoder structure and faster RCNN model were used to detect water edge and identify obstacles on the surface of the water to build environment model around unmanned surface vehicle． Then local path planning method based on VFH + was brought to construct the primary histogram，binary histogram and mask histogram to compress the environment data，and a reasonable cost function was introduced to obtain the effective obstacle avoidance direction for unmanned surface water． Results of simulation experiments based on MODD dataset and obstacle avoidance experiments in real ship show that the algorithm can effectively extract the water surface image information and obtain a reasonable local path planning strategy to lead a smooth obstacle avoidance path at 10kn， which meets the obstacle avoidance requirements of unmanned surface vehicle．