# Industrial Application of AI-Based Assistive Magnetic Particle Inspection
## 2 Related Work  
A substantial number of works have been found in the literature on the MPI topic. For instance, [4] shows an industrial application of a conventional computer vision algorithm on a rails wheelset. The non-standalone system built around a camera and a small lighting system must be coupled with an external PC for image processing, moreover, PoD results are not given and only qualitative examples are shown. Another example of industrial automatic defect detection in MPI is given by [5], showing material selection for specific image capturing on MPI. As stated in this article, optical filters are mandatory, and thus were used with the PARADES machine. A mobile machine based on a robot is depicted in [6] for use with MPI control on wide parts. It shows an interesting embedded magnetic excitation material as well as an MPI suspension dispenser. The defect-detection feature is not developed and shows the need for an external computer in order to process acquired images, due to the embedded Raspberry Pi Zero low computational performance.  
The study in [7] is another example of an industrial machine used to detect defects with the MPI technique along with computer vision. It uses hardware as well as the usual computer vision algorithms to detect flaws. The paper focuses on detection speed rather than Percentage of Detection (PoD) and shows an insufficient detection accuracy (around 50%) for flaws less than 10 mm.  
An experimental hardware setup is presented in [8] and aims to improve defect-detection precision using a conventional computer vision algorithm based on Hough transform. It visually shows how the false detections are removed but quantitative metrics are not given. Another approach was taken in [9], in order to find the 3D reconstruction of cracks on parts. The result is quite similar detection conditions as in PARADES, with defect size representing 1/4000 of the entire image with a few pixels for the defect width (7 pixels in their case). The scene area seems a lot smaller than ours, with a 7-pixel defect width at 2046 \(\times\) 2040 full resolution compared to the PARADES high-definition \(>\) 96 Mpc camera system.  
An experimental platform is detailed in [10] and uses conventional image processing (i.e., a Canny operator); however, it cannot be used in a production line. Another example of an AI-based defect-detection system is presented in [11], which uses a CNN coupled with a set of cameras to find defects on a specific crankset. A 3000 \(\times\) 4096 pixel camera is used to feed a ResNet34 CNN, which has been trained on 100 \(\times\) 100 image patches. This study shows a very high score on classification and a 0.9 [EMAIL_ADDRESS] (mAP computed with IoU score over 0.5) for object detection. A segmentation AI-based method is proposed in [12].  
Study [13] shows a good 96.5% recall with 91.7% precision on industrial bearing using a custom mobileNetV3 CNN but the hardware setup is not presented.  
To summarize, a lot of work has been done on MPI using image processing and deep-learning methods, achieving better performance than human controllers while lowering risks and improving working conditions. However, most of the studies concentrate on developing the software side with a laboratory minimal hardware setup most of the time. The literature is missing industrial-grade production-ready hardware for MPI automatic defect detection. The goal of this paper is to present the PARADES machine, which is designed as a versatile platform with industrial production use in mind and with a focus on AI-ready image acquisition. The PARADES machine aims to achieve the best detection performance in MPI but might be sufficiently generic for a variety of industrial computer vision tasks.