ICT Research Institute, DGIST, Daegu 42988, Korea.
School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Korea.
Sensors (Basel). 2021 Aug 4;21(16):5279. doi: 10.3390/s21165279.
The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.
全球范围内，海藻的消费逐年增加。因此，海藻的异物检测变得越来越重要。海藻中混合了紫菜和马尾藻等各种材料，因此即使是同一种海藻也有各种颜色。此外，海藻表面凹凸不平且油腻，经常产生漫反射。由于这些原因，海藻中的异物很难被检测到，因此实际制造现场使用的传统异物检测器的准确率不到 80%。在检测异物时，还应考虑实时检测。由于海藻需要大规模生产，因此快速检查至关重要。然而，高光谱成像技术通常不适用于高速检查。在本研究中，我们通过使用降维和简化操作来克服这一限制。为了提高准确性，所提出的算法分两个阶段进行。首先，使用减法方法清楚地区分海藻和输送带，同时检测一些相对容易检测到的异物。其次，基于减法方法的结果进行标准化检查。在此过程中，所提出的方案采用了减法、除法和逐一匹配等简化且无负担的计算，实现了准确性和低延迟性能。在评估性能的实验中，使用了 60 个正常海藻和 60 个含有异物的海藻，所提出算法的准确率为 95%。最后，通过将所提出的算法实现为异物检测平台，确认了在快速检查中实时运行的可能性，并确认了在实际制造现场部署的可能性。
