激光雷达与机器视觉融合的海面目标检测方法

Sea Surface Target Detection Method Fusing Lidar and Machine Vision

针对近岸复杂环境和目标部分遮挡条件下海面目标检测易出现目标漏检和虚警的问题,提出了一种激光雷达与机器视觉融合的海面目标检测方法。首先,设计了一种基于注意力机制与可变形卷积的特征提取模块,提高YOLOv7-tiny网络对海面障碍物目标特征的提取能力,从而降低近岸复杂背景干扰导致的漏检率和虚警率;然后,将激光雷达聚类结果和改进的YOLOv7-tiny网络模型预测结果进行融合,降低目标部分遮挡导致的漏检率;最后,在海面目标检测图像数据集上进行实验验证,结果表明,与原YOLOv7-tiny网络模型相比,改进YOLOv7-tiny网络模型的mAP提升了3.8个百分点,在目标部分遮挡场景下用实船实验数据进行验证,与NMS算法相比,所提融合方法的漏检率降低了6.9个百分点,验证了所提方法能够在近岸复杂环境和目标部分遮挡场景下,降低海面目标检测的漏检率和虚警率。

Aiming at the problem of missed target detection and false alarms in sea surface target detection in complex nearshore environments and partial target occlusion conditions,a sea surface target detection method fusing lidar and machine vision is proposed.Firstly,a feature extraction module based on attention mechanism and deformable convolution is designed to improve the ability of YOLOv7-tiny to extract features of sea surface obstacle targets,thereby reducing the missed detection rate and false alarm rate caused by complex nearshore background interference.Then,the lidar clustering results and the improved YOLOv7-tiny network model prediction results are fused to reduce the missed detection rate caused by partial occlusion of the target.Finally,experimental verification is conducted on the sea surface target detection image data set.The results showed that compared with the original YOLOv7-tiny network model,the mAP of the improved YOLOv7-tiny network model is increased by 3.8 percentage points.Experimental verification is conducted on real ship experimental data in a scene where the target is partially occluded.Compared with the NMS algorithm,the missed detection rate of the proposed fusion method is reduced by 6.9 percentage points,which verifies that this method can reduce the missed detection rate and false alarm rate of sea surface target detection in complex nearshore environments and partially blocked target scenarios.