Road Pothole Detection using Deep Learning | Python Final Year Project 2024 | IEEE Project

Road Pothole Detection using Deep Learning | Python Final Year Project 2024 | IEEE Project 2024 - 2025.
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📌Project Title: Road Pothole Detection using Deep Learning
💡Implementation: Python.
🔬Algorithm / Model Used: YOLOv8 Architecture.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/

📌IEEE Base Paper Title: Road Pothole Detection Based on Crowdsourced Data and Extended Mask R-CNN.

📌IEEE Base paper Abstract:
Road pothole detection is significant for road maintenance which has been widely solved based on computer vision models in recent years. However, the accuracy of the pothole detection is still far from satisfactory and has the potential to be increased. One of the challenges is the diversity of the training samples. Another challenge is the extension of mature computer vision models. Therefore, in this paper, we created a pothole dataset with multiple road conditions and environments via samples of previous studies and developed an extended mask R-CNN model. Based on the created dataset, we compared the proposed model with the traditional model using the ResNet- 50 and ResNet-101 networks. The experimental results show a mAP0.5 of 92.1% on the test dataset, surpassing the traditional backbone networks ResNet-50 and ResNet-101 by 3.6% and 4.5%, respectively. Furthermore, we compared the details of the different models. The experimental results show that the total area prediction error is only 3.19% on the test dataset. It suggests that our model can precisely extract geometric features and the area information of detected potholes.

📌REFERENCE:
Linchao Li, Jiazhen Liu, Jiabao Xing, Zhiyang Liu, Kai Lin, and Bowen Du, “Road Pothole Detection Based on Crowdsourced Data and Extended Mask R-CNN”, IEEE Transactions on Intelligent Transportation Systems (Early Access), 2024.

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[Rootvik.]

I just took Btech CS with AIML for my first year and this video popped up! ❤