mlsanityData to 14 Oct 2026

Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks

arXiv 2208.05192 ↗First posted 10 Aug 2022cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component. One of the most effective ways to monitor the AirTender functioning is to look for oil stains on its surface. Starting from real-time images, AirTender is first detected in the motorbike suspension system, simulated indoor, and then, a binary classifier determines whether AirTender is spilling oil or not. The detection is made with the help of the Yolo5 architecture, whereas the classification is carried out with the help of a suitably designed Convolutional Neural Network, OilNet40. In order to detect oil leaks more clearly, we dilute the oil in AirTender with a fluorescent dye with an excitation wavelength peak of approximately 390 nm. AirTender is then illuminated with suitable UV LEDs. The whole system is an attempt to design a low-cost detection setup. An on-board device, such as a mini-computer, is placed near the suspension system and connected to a full hd camera framing AirTender. The on-board device, through our Neural Network algorithm, is then able to localize and classify AirTender as normally functioning (non-leak image) or anomaly (leak image).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 65 references that are in the map. First posted on arXiv 10 Aug 2022.

Builds on

24 of 65 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  6. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  7. 2017Mask R-CNNcited by 7,277
  8. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  9. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  10. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  11. 2017Squeeze-and-Excitation Networkscited by 4,789
  12. 2014Going Deeper with Convolutionscited by 4,241
12 more
  1. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  2. 2018YOLOv3: An Incremental Improvementcited by 2,659
  3. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  4. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  5. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  6. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  7. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  8. 2013Network In Networkcited by 605
  9. 2018Object Detection with Deep Learning: A Reviewcited by 528
  10. 2018A high-bias, low-variance introduction to Machine Learning for physicistscited by 108
  11. 2016Crafting GBD-Net for Object Detectioncited by 6
  12. 2020Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnosticscited by 4

Built on by

Not cited yet.