Project : Oculus_cAR

I have implemented the YOLOv4 algorithm to create custom trained weights for desired object detections(in our case Classes detected are: Car, Vehicle, Stop Signs, Person, Animals). and sliding window technique is merged to detect lanes.

Steps Involved in Implementing Custom trained YOLOv4 Detection in Google CoLab!

1. Build Darknet
2. Perform Detections with Darknet and YOLOv4 on Pre-trained weights
3. Training a Custom YOLOv4 Object Detector in the Cloud
4. Gather and Label Custom Dataset
5. Train Custom Object Detector

Steps Involved in Lane detection!

1. Pre-processing of Image
2. Perspective transform
3. Applying Sliding Windows Technique.
4. Curve fitting
5. Inverse perspective transform
6. Plotting curve on input image/frame.

Input Image

Object Detection through Custom trained YOLOv4

Testing our object detection model on IISc campus:

Input:

Output:

Link to full Video: https://user-images.githubusercontent.com/111170719/206981021-ce4624af-9dc6-489a-b2ee-f48bda1bfbbc.mp4

Testing our object detection model on Random Delhi Highway YouTube video:

Input:

Output:

Link to full Video: https://user-images.githubusercontent.com/111170719/207027449-07eaaf4d-de20-4d43-bb5b-9dd183e48bdc.mp4

Testing our Lane detection and object detection model on Random sample video from YouTube:

Link to full Video: https://user-images.githubusercontent.com/111170719/206981713-f24840b8-fff4-4e2c-a71f-89946065f8de.mp4

Quantitative Evaluation:

Plot of Loss Curve with Iterations-

NOTE: Due to limitation on GPU usage for training on Google colab the loss curve is discontinous in between

FUTURE PLANS: THE MODEL WILL BE TRAINED ON MOTORCYCLE AND TRAFFIC SIGNAL DATASETS ALSO.