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Road-Asset-Detection

The project aims to develop a Road Asset Detection system using convolution neural network. We had modified existing tiny YOLO model for the detections.

Our project is an extended work on the blooming Computer Vision on localization of relevant objects. We implement a real-time system capable of accounting for different road assets like traffic signs,potholes,lanes,zebra lines and vehicles . We have used the YOLO model for this purpose.We used this model as they have superior localization and classification in addition to easier modification of layers,via transfer learning.We further seek to implement a fully functional real time system on Indian Roads.

Epochs 120

Dataset size

                 Pascal VOC: 2029
                 Potholes : 630                     
                 LISA: 6618

Accuracy

                  Pothole Detection :84
                  Vehicles :88
                  Traffic Signs:87

This accuracy can be increased further by adding more images to the dataset and training them further.

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