State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
COMAC ShangHai Aircraft Design and Research Institute, Shanghai 201210, China.
Sensors (Basel). 2019 Feb 4;19(3):644. doi: 10.3390/s19030644.
Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers' first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.
汽车表面的缺陷，如划痕或凹痕，会在制造和跨境运输过程中出现。这会影响消费者的第一印象和汽车本身的使用寿命。在全球大多数汽车行业中，检测过程主要由人眼来完成，但这种方法既不稳定又不充分。人工智能与汽车行业的结合如今具有广阔的应用前景。然而，由于光照不均匀、镜面高光反射、各种反射模式和有限的缺陷特征，在计算机系统中检测此类缺陷仍然是一个挑战。本文提出了一种新颖的自动检测系统（AIS），用于检测位于或靠近车身风格线、边缘和把手附近的汽车表面缺陷。该系统由图像采集和图像处理设备组成，在封闭的环境中以非接触的方式工作，使用四个 LED 光源。具体来说，我们使用五个面阵 CCD 摄像机同步采集汽车五个侧面的图像。然后，AIS 通过多尺度 Hessian 矩阵融合方法从车身图像中提取候选缺陷区域。最后，通过特征提取（形状、大小、统计和离散特征）和支持向量机算法，将候选缺陷区域分为伪缺陷、凹痕和划痕。实验结果表明，自动检测系统可以有效减少由图像噪声引起的伪缺陷的误检，凹痕缺陷的准确率达到 95.6%，划痕缺陷的准确率达到 97.1%，非常适合用于海关对进口车辆的检验。
