Roy, A. and Bagade, P. (2024). Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 492-499.

1. Attentive-YOLO: On-site Water
Pipeline Inspection using Efficient
Channel Attention and Reduced
ELAN-based YOLOv7
Atanu Shuvam Roy | Priyanka Bagade
VISAPP 2024 : 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
20
24
20
24

2. Authors
Priyanka Bagade
I am an assistant professor in the Computer Science and Engineering
department at IIT Kanpur. Before that, I worked as a software architect
in Intel Corporation, USA for 5 years. I have completed Ph.D. in
Computer Science from Arizona State University under the guidance of
Prof. Sandeep K. S. Gupta. My current research focuses on the internet
of things, deep learning, and cyber-physical systems.
Atanu Shuvam Roy
I am currently pursuing MTech student at the Indian Institute Of
Technology Kanpur in Computer Science and Engineering. I have
completed my Bachelor’s from Jiangxi University of Science and
Technology from the same field. I am doing research in Embedded
Systems and Internet of Things (IoT)

3. The Problem
05
What is the problem statement
and motivation behind the project
?
Existing Works
02
What other’s have done ? What
are their techniques ?
Methodology
03
What have we done ? What are
the steps followed ?
Experimentation
04
Hardware requirements and
Experimental Setup
Results
01
Implementation and results of the
experiments in real world
scenarios
Conclusions
06
What is the conclusion ? And
What are the future work ideas
Table of contents

4. 01
Problem Statement
20
24
20
24

5. Real-time water pipeline defect detection on edge
devices
Problem Statement

6. Motivation
• Traditional water pipe inspection methods are costly and labour
intensive, leading to delayed defect detection.
• Current techniques like radiography and CCTV require on-site
experts and lack real-time automatic detection capabilities.
• YOLOv5 models offer acceptable performance but are bulky,
hindering fast inference on edge devices.
• There's a need for lightweight and efficient models to streamline
inspection, reduce costs, and enable real-time defect detection
for better water distribution management
20
24
20
24
https://revogroupau.com/2022/04/21/ndt-inspections-of-pipelines/
https://lmats.com.au/services/advanced-ndt-solutions/gw-lrut-guided-wave-long-range-
ultrasonic-testing

7. What can the defects look like ?
Encrustatio
n
Ferrul
e
Ston
e
Deformatio
n
Sludge
Formation
Root
s

8. 02
Existing Works
20
24
20
24

9. Image Processing Techniques:
● Kohonen clustering network [1]
● Canny Edge detection [1]
● Mathematical morphological operator algorithm [2]
● CCTV images based on segmentation[2]
● Otsu's picture segmentation [3]
● Hue, Saturation, and Intensity [2]
Machine Learning Based Techniques:
● Adaboost [4]
● Random Forest [4]
● Rotation Forest [4]
● SVM [5]
State of the Art
[1] Prema Kirubakaran, A., and I. V. Murali Krishna. "Pipeline crack detection using mathematical morphological operator." Knowledge Computing and its Applications: Knowledge Computing in Specific Domains: Volume II (2018): 29-46.
[2] Bondada, Venkatasainath, Dilip Kumar Pratihar, and Cheruvu Siva Kumar. "Detection and quantitative assessment of corrosion on pipelines through image analysis." Procedia Computer Science 133 (2018): 804-811.
[3] Zhong, Xingu, et al. "Assessment of the feasibility of detecting concrete cracks in images acquired by unmanned aerial vehicles." Automation in Construction 89 (2018): 49-57.
[4] Wu, Wei, Zheng Liu, and Yan He. "Classification of defects with ensemble methods in the automated visual inspection of sewer pipes." Pattern Analysis and Applications 18 (2015): 263-276.
[5] Halfawy, Mahmoud R., and Jantira Hengmeechai. "Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine." Automation in Construction 38 (2014): 1-13.

10. Deep Learning Based Techniques:
● Convolutional neural network (CNN) [6]
● YOLOv3 [7]
● YOLOv4 [8]
● Improved YOLOv4 with spatial pyramid pooling (SPP) [9]
● Enhanced YOLOv5 [10]
● Cycle-GAN [10]
● Migration learning with channel pruning techniques [11]
State of the Art
[6] Zhang, Jiawei, et al. "Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques." Applied Sciences 13.7 (2023): 4589.
[7] Zhao, Xinhua, Xue Wang, and Zeshuai Du. "Research on detection method for the leakage of underwater pipeline by YOLOv3." 2020 IEEE international conference on mechatronics and automation (ICMA). IEEE, 2020.
[8] Zhou, Zhong, Junjie Zhang, and Chenjie Gong. "Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network." Computer‐Aided Civil and Infrastructure Engineering 37.6 (2022): 762-780.
[9] Zhang, Jiawei, et al. "Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques." Applied Sciences 13.7 (2023): 4589.
[10] Chen, K.; Li, H.; Li, C.; Zhao, X.; Wu, S.; Duan, Y.; Wang, J. An Automatic Defect Detection System for Petrochemical Pipeline Based on Cycle-GAN and YOLO v5. Sensors 2022, 22, 7907. https://doi.org/10.3390/s22207907
[11] Situ, Zuxiang, et al. "Real-time sewer defect detection based on YOLO network, transfer learning, and channel pruning algorithm." Journal of Civil Structural Health Monitoring (2023): 1-17.

11. ● Does not account for low powered devices
● Models usually large in size
● Complex Computation
● Realtime not possible in edge devices
Challenges in current techniques

12. 03
Methodology
20
24
20
24

13. We Propose
● An Attentive-YOLO model based on the state-of-the-art object detection YOLOv7 model
with a reduced Efficient Layer Aggregation Network (ELAN).
● A lightweight attention model in the head and backbone of the YOLOv7 network to improve
accuracy while reducing model complexity and size.
● An efficient model to be deployed on edge devices such as the Raspberry Pi to be used in
Internet of Things (IoT) systems and on-site robotics applications like pipeline inspection
robots.
Proposed Methodology

14. The YOLO Model
Methodology
• Reducing ELAN structure by having fewer convolution layers
• Implementation of ECAM at the end of the backbone layer
with an additional convolution layer
• The addition of ECAM to the head of the network before the
concatenation with the P5 and P4 layers of the backbone

15. ● The input characteristics are multiplied together and then
used as the input for the next layer. Equation 1 shows how
this method uses function adaptation to find the Kac
value, and its proportionality to the dimension of the
channel - C:
where, λ = 2, b = 1 and Kac accepts nearest odd value
● R-ELAN has three convolutional blocks. Afterwards, layers
from the previous are passed through a maxpool layer,
concatenated, and then passed through another
convolutional layer
● Incorporate with convolutional blocks at the previous and
next creating a ECAM Block
Methodology (Contd.)

16. Final Model

17. 04
Experimentation
20
24
20
24

18. Experimental Setup
Simulation
A: shows the simulated setup of the device
with a USB webcam connected to
Raspberry-Pi system.
B: shows the prediction results after being
run. For the real-world simulation, the video
of the pipe was directly fed to the
Raspberry Pi as a file and via webcam.
HARDWARE USED
• Raspberry Pi 3B+
• Logitech Webcam

19. 05
Results
20
24
20
24

20. Results

21. Results (contd.)

22. Results (contd.)

23. Based on the experiments, the proposed model, Attentive-YOLO, achieves a mAP score
of 0.962 over 0.93 (1/3rd channel width) compared to the Yolov7-tiny model, with an
almost 20% decrease in model parameters.
Results in a Glance

24. 06
Conclusion
20
24
20
24

25. Summary
• We addressed the challenge of inspecting and repairing water pipes by leveraging computer vision
techniques for non-destructive testing.
• We proposed a modified version of the YOLOv7 Tiny model, incorporating the R-ELAN with ECAM into the
base architecture.
• We created an efficient model suitable for deployment on small-scale computing devices like the Raspberry
Pi in IoT and robotics applications such as pipeline inspection robots.
• Our performance evaluation demonstrates that the proposed model, Attentive YOLO, outperforms the base
YOLOv7 and YOLOv7-Tiny models on a single-chip computer regarding inference speed and Frames Per
Second (FPS).
• The base YOLOv7 model failed to run on the device, leading to system crashes, while the YOLOv7-Tiny model
exhibited slow inference times, taking nearly 4 seconds per frame.
• Attentive YOLO achieves an average inference time of approximately 0.9 seconds per frame on the Raspberry
Pi for the full-depth model, corresponding to nearly 1 FPS on average on the shallow model while retaining
considerable accuracy compared to state-of-the-art models.

26. • Fine-tuned the model on domain-specific data for enhanced performance in pipeline
inspection tasks like crack and corrosion detection.
• Experiment with different model architectures to improve accuracy, efficiency, and
robustness in diverse pipeline conditions.
• Conduct field trials to assess the model's performance in various operational conditions
and pipeline types.
• Evaluate seamless integration of the model into pipeline inspection robots' software and
hardware.
• Teste the model on the Sewer-ML Large Dataset for sewer pipe inspection tasks, including
defect identification.
Future Work

27. 20
24
20
24
Thanks!
Do you have any questions?
atanusroy22@iitk.ac.in | pbagade@iitk.ac.in