This week, I worked on implementing edge detection for separating the fruit from the background. We may need this when we introduce multiple fruits so that we can separate the fruit from the background, detect which fruit it is by examining the colour within the designated area, and accordingly running our algorithms. Here are the results of running edge detection on the image of this banana:
I developed the first classifier for our product, which is the percentage area rottenness classifier. This classifier considers the good parts of the fruit and the seemingly bad parts and computes the percentage rottenness of the fruit. If the percentage is above a certain threshold, it will classify fruits as rotten, else it will classify them as good. Ishita Kumar worked on segmenting the good versus bad parts of the banana, and then, I pass that result into my classifier so that the image can be classified. Over the next week, I plan on looking into an optimal threshold, which I can obtain by testing the classifier on a large number of Google images, as well as some manually taken images if possible. We have found a Kaggle dataset containing images of rotten versus good bananas, so I plan on looking into using that to determine a threshold.
I also looked into AlexNet to work on developing the second classifier. I have started working on it and plan to have it working in the coming week. I can test its working using the Kaggle dataset.
Regarding the use of the 2 classifiers and seeing which one is optimal, I was thinking that instead of using one of the classifiers, we could instead first run the percentage rottenness algorithm. If the percentage is above the “rotten” threshold, we classify it as rotten. If it’s under a certain “good” threshold, we classify it as good. However, if it is in a gray area between these 2 thresholds, we can classify it using the AlexNet classifier. I still need to discuss this with my teammates and the instructors. This approach would also depend on how well the AlexNet classifier seems to work.
As part of the team, we all started working on the conveyor belt this week and plan to have it done by the end of next week. My progress, as well as the team’s progress, is on schedule so far.