We develop a machine vision system as a key component of a robot‐assisted packaging system, which can guide the robot arms to pack the roast sauries into cans. For gripping strategy generation, the system is required not only to be able to detect the roast saury area but also estimate the geometric parameters. Besides, according to different canning requirements, it is also necessary to distinguish the type of fish parts. Facing these challenges, we propose a novel rule‐based matching method combined with an improved efficient graph‐based image segmentation (EGIS) method for sensing the fish part. Specifically, the matching method applies our originally designed rule‐based similarity under a genetic algorithm framework combined with deterministic crowding technique, which is used to sensing one type of fish parts. On the other hand, we improve the EGIS by introducing a shape restriction to deal with leftover fish parts. The experiments are implemented for two different types of fish part in the real factory environment. The result of our method achieved a mean location accuracy of 93.5% with a practical average processing time of 2.6 s per image. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

中文翻译：

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使用基于规则的匹配和分段以鱼感测罐头的视觉系统

我们开发了机器视觉系统，将其作为机器人辅助包装系统的关键组成部分，该系统可以引导机器人手臂将烤鱼秋刀鱼包装到罐中。为了产生抓握策略，该系统不仅需要能够检测烤鱼秋刀鱼区域，而且还需要估计几何参数。此外，根据不同的罐头要求，也有必要区分鱼的种类。面对这些挑战，我们提出了一种新颖的基于规则的匹配方法，并结合了一种改进的基于有效图的图像分割（EGIS）方法来检测鱼的部分。具体来说，匹配方法是在遗传算法框架下结合确定性的拥挤技术，运用我们最初设计的基于规则的相似性，用于检测一种鱼的部位。另一方面，我们通过引入形状限制来处理剩余的鱼部分来改善EGIS。实验是在实际工厂环境中针对两种不同类型的鱼类进行的。我们的方法的结果实现了平均定位精度93.5％，每个图像的实际平均处理时间为2.6 s。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布