基于脉冲耦合神经网络的弹簧卡箍缺陷检测

传统的弹簧卡箍缺陷多为产后人工全检,存在漏检与缺陷率上升等现象,这不但会使成本上升、也对人力资源提出了考验。为此实现自动实时在线全检就成为急需解决的课题,设计了基于机器视觉的弹簧卡箍在线自动检测系统,该系统安装在弹簧卡箍流水线两侧,搭建特定光源,通过激光传感器外部触发工业相机对其表面进行图像捕获,送上位机进行缺陷判定与定位,最后通过RS485将判定结果送下位机来控制剔除机制。实验结果显示：该系统采用改进的脉冲神经网络(PCNN)能准确提取目标缺陷区域并对缺陷进行判定,可在0.348 s每个零件的速度下,检测出弹簧卡箍表面大于10像素的缺陷。通过对不同弹簧卡箍进行检测验证实验,证明了PCNN算法对缺陷分割的准确性和有效性。

A spring clamp detection system based on PCNN

The traditional defect detection method of spring clamps is manual detecting after production, which causes higher misjudgment rate and deficiency rate, leads to higher costs and brings tougher challenges to the human resources. This causes an increasing demand for automatic online detection system and computer vision plays a leading role in this growing field. In this paper, the automatic real-time detection system of the clamps based on machine vision is designed. This system is situated on both sides of the production line. It hardware is composed of a specific light source, a laser sensor, an industrial camera, a computer and a rejecting mechanism. The camera begins to capture an image of the clamp once triggered by the laser sensor. The image is then sent to the computer for defective judgment and location through Gigabit Ethernet (GigE), after which the result will be sent to rejecting mechanism through RS485 and the unqualified ones will be removed. Experiments on real-world images demonstrate the pulse coupled neural network can extract the defect region and judge defect. It can recognize any defect greater than 10 pixels under the speed of 2.8 clamps per second. Segmentations of various clamp images are implemented with the proposed approach and the experimental results demonstrate its reliability and validity.