Autonomous lane detection for self-driving cars using Hough lines, Masking, Canny filters, and Gaussian filters. Primary libraries used in this project are OpenCv, NumPy, and matplotlib. This technique has a lot of real-life applications including self-driving cars for detecting lanes accurately and traversing accordingly.
The steps involved in this project are listed below:
- Importing the required Libraries
- Conversion of RGB to Grayscale
- Gaussian Blur
- Canny Filters
- Masking
- Combining Canny filters and masking images
- Hough lines
- Output image
The Method consists of 1 python script- canny_edge.ipynb which executes the proposed algorithm.
Some assumptions/ shortcomings of Method 1 include:
- Masking of the desired region of interest (ROI) which will vary from one vehicle to another.
- Filters and edge detectors tend to perform poorly in high steep areas.
- Shadows, glares and rapid movement of the vehicle results in poor frame clarity
Lane detection for autonomous vehicles with the help of Convolutional Neural Networks (CNN) is experimented with in this section. The reasons/ shortcomings of Method 1 is discussed above and thus, CNNs where used to increase the robustness and reliability of the system.
The pipeline of the architecture reflected in this Method is:
- Data-Preprocessing
- Building Convolutional Neural Net
- Training Model
- Saving Model
- Prediction in real-time
The Method consists of 3 python scripts- fully_conv_NN.py (training), draw_detected_lanes.py (testing) and test.ipynb for the ease of access. The first python script will train the network and save the model in h5 format (full_CNN_model.h5). The second python script will implement the detection of lines in the input image/ video and third python script will test the model.