基于机器视觉的航标倾斜检测方法

    Detection method of the buoy tilt based on machine vision

    • 摘要: 航标受风浪、气候或撞击等因素的影响,往往导致倾斜,而大幅度倾斜可能会引起航标本体的损坏。传统的航标巡检方法主要依靠人力,不仅耗时耗力,而且苛刻的作业条件威胁着作业人员的安全。因此,设计一种智能检测航标损坏的方法对保障作业人员安全和提高海事监管效率具有重要意义。为解决在海面实际作业中,硬件设备计算能力有限,导致检测速度过慢、精度低等问题,提出一种基于机器视觉的轻量化航标倾斜检测方法。在单阶段检测网络YOLOv5的基础上,引入环形平滑标签(CSL)技术,将角度回归问题转变为分类问题,提出R-YOLO航标倾斜检测模型;采用轻量化GhostNet特征提取模块减少模型参数以适用实际作业环境;加入注意力机制模块(CBAM)以提高航标倾斜检测模型精度。试验结果表明:改进的倾斜检测方法与R-YOLO相比,精度提升5.6%,参数量减少2.74×106,最高的检测精度可达93.2%。

       

      Abstract: Buoys are affected by wind,waves,weather or impacts,often resulting in tilting,and substantial tilting may cause damage to buoys.The traditional method of buoy inspection mainly relies on manpower,which is not only timeconsuming and labor-intensive,but also the harsh operating conditions threaten the safety of operators.Therefore,designing an intelligent method for detecting damage to navigational markers is of great significance for safeguarding the safety of operators and improving the efficiency of maritime supervision.Considering the limited computational capability of hardware devices in the actual operation on the sea surface,which leads to the problems of slow detection speed and low accuracy,a lightweight buoy tilt detection method based on machine vision is proposed.Firstly,based on the single-stage detection network YOLOv5,CSL(Circular Smoothing Labels) transform the angle regression problem into a classification problem and proposes the R-YOLO buoy tilt detection model.Then.the lightweight GhostNet feature extraction module is added can decrease the model parameters to suit the actual operational environment.Finally,a CBAM(Convolutional Block Attention Module) attention mechanism module can improve the accuracy of the buoy tilt detection model.The experimental results demonstrate the improved tilt detection method in this paper improves the accuracy by 5.6% and reduces the parameter quantity by 2.74×106 in comparison with R-YOLO,and the highest accuracy can reach 93.2%.

       

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