11 Aug

The video in the last post depicted a very slow detection of the ball. I also tested a CPU-trained model but, as expected, it was quite slow: To improve the performance of the algorithm, I replaced the original FasterRCNN model (torchvision.models.detection.fasterrcnn_resnet50_fpn()) with a mo...

11 Aug

I merged the ball detection code with the distance estimation code and added a camera component to make it live. The following video shows the code running live on CUDA: Model prediction is very slow on the CPU; I am currently checking out techniques such as quantization to optimize the model. A...

28 Jul

We had learned from the non-ML-based ball detection algorithm that a color-and-contour-based algorithm cannot always detect the ball with enough accuracy. As a result, I have been developing an ML-based platform for training a model with multiple images of the ball and identifying a bounding box aro...

05 Jul

The ball detection algorithm (without the use of a trained model) has been completed. Following the work posted in the last blogpost, I realized that it may be a better idea to isolate the ball away from its environment and determine its bounding box in the cropped image. The first analysis of the b...

29 Jun

Over the last two weeks, I have been working on the existing code by Parth Agrawal for the estimation of the location of the green ball from images that were shot at different angles. The code had estimated the position of the ball quite well for the hi-def images but not so for the 800x600 images:...

30 Jul

Recently, I've been looking into methods to potentially improve the depth map produced by an Intel RealSense D435i, and OpenCV's disparity filter ([1], [2], [3]) seemed like a good initial starting point. At a high level, after generating a disparity map between left and right camera images vi...

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