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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
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Posts
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portfolio
Light Field Saliency Detetion
I experimented on methods to reduce the high computational power requirement of deep-learning based light field saliency detection algorithms and developed fast and accurate light field saliency detection algorithm with low computational complexity, leveraging on RGB saliency detectors. Our model demonstrated state-of-the-art performance compared to light field saliency detection models in terms of speed with comparable and better Fβ values
Two collaborating robotic platforms for Sri Lanka Robotics Challenge 2018
We developed two robotics platforms collaborating with each other to carry out the necessory tasks in the arena. You can check the video of the first round of the competition here 
Traffic Sign, Traffic Light and Static Object Detection for Self-driving.
We developed traffic sign, traffic light, lane and road marking detection algorithms suitable for chaotic and unstructured road scenarios in Sri Lanka and created the first large datasets for traffic sign, traffic light, and road marking detection for Sri Lanka, containing unique challenges in a developing country with scenarios covering traffic, rain, dazzle light, and normal conditions. Our row-wise classification-based lane marking detection algorithm which outperforms state-of-the-art in terms of speed with comparable and better F1 values and the end-system was optimized for real-time performance in Nvidia-Jetson Xavier with ROS.You can check the video of the final system here here.
Coin Collecting and Bridge Unfolding Robot
We developed a robotic platform with line and wall following, grid solving, and coin collecting capabilities with a 1 DoF arm to carry out the tasks in the arena. This was carried out as a part of the undergraduate module: Robot Design and Competition. You can check the video of the final demo run here. 
Mobile Robot for Xbotix 2018 Robotics Competition 2018
We developed a robotic platform with line and wall following, grid solving, object detection, and colour detection capabilities with a mechanical gripper to carry out the tasks in the arena. You can check the video of a trial run here. 
Custom Application Processor for Image Downsampling
Designed an application specific processor with a custom ISA for processing RGB images using DE2-115 development board. Final processor could downsample an image by an integer factor up to 15 using Gaussian and average filtering and it could apply any linear separable filter to images.UART transceiver, developed using Verilog was used as the communication medium between the processor and the PC. You can check the report and the code here.
Analog Function Generator
We developed a function generator device using analog circuits to generate given waveforms (sine, sawtooth, square, and triangular). Final device had the capability to produce the waveforms in the range 13-22800 Hz, with 0-20V amplitude and it was able to provide a current up to 0.2 A.. You can check the report and the datasheet here and schematics here.
publications
Fast and Accurate Light Field Saliency Detection through Deep Encoding
Published in arXiv, 2021
Light field saliency detection—important due to utility in many vision tasks—still lacks speed and can improve in accuracy. Due to the formulation of the saliency detection problem in light fields as a segmentation task or a memorizing task, existing approaches consume unnecessarily large amounts of computational resources for training, and have longer execution times for testing. We solve this by aggressively reducing the large light field images to a much smaller three-channel feature map appropriate for saliency detection using an RGB image saliency detector with attention mechanisms. We achieve this by introducing a novel convolutional neural network based features extraction and encoding module. Our saliency detector takes 0.4 s to process a light field of size 9×9×512×375 in a CPU and is significantly faster than state-of-the-art light field saliency detectors, with better or comparable accuracy. Furthermore, model size of our architecture is significantly lower compared to state-of-the-art light field saliency detectors. Our work showsthat extracting features from light fields through aggressive size reduction and the attention mechanism results in a faster and accurate light field saliency detector leading to near real-time light field processing.
Recommended citation: Hemachandra, Sahan & Rodrigo, Ranga & Edussooriya, Chamira. (2021). "Fast and Accurate Light Field Saliency Detection through Deep Encoding" arXiv. 1(3). https://arxiv.org/abs/2010.13073
SwiftLane: Towards Fast and Efficient Lane Detection
Published in International Conference on Machine Learning Applications, 2021
Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources. In this work, we propose SwiftLane: a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. This framework is supplemented with a false positive suppression algorithm and a curve fitting technique to further increase the accuracy. Our method achieves an inference speed of 411 frames per second, surpassing state-of-the-art in terms of speed while achieving comparable results in terms of accuracy on the popular CULane benchmark dataset. In addition, our proposed framework together with TensorRT optimization facilitates real-time lane detection on a Nvidia Jetson AGX Xavier as an embedded system while achieving a high inference speed of 56 frames per second.
Recommended citation: Jayasinghe, Oshada & Anhettigama, Damith & Hemachandra, Sahan & Kariyawasam, Shenali & Rodrigo, Ranga & Jayasekara, Peshala. (2021). "SwiftLane: Towards Fast and Efficient Lane Detection." International Conference on Machine Learning Applications. 1(2). https://arxiv.org/pdf/2110.11779.pdf
CeyMo: See More on Roads – A Novel Benchmark Dataset for Road Marking Detection
Published in WACV 2022, 2022
In this paper, we introduce a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailability of an evaluation script, lack of annotation formats and lower resolutions. Our dataset consists of 2887 total images with 4706 road marking instances belonging to 11 classes. The images have a high resolution of 1920 x 1080 and capture a wide range of traffic, lighting and weather conditions. We provide road marking annotations in polygons, bounding boxes and pixel-level segmentation masks to facilitate a diverse range of road marking detection algorithms. The evaluation metrics and the evaluation script we provide, will further promote direct comparison of novel approaches for road marking detection with existing methods. Furthermore, we evaluate the effectiveness of using both instance segmentation and object detection based approaches for the road marking detection task. Speed and accuracy scores for two instance segmentation models and two object detector models are provided as a performance baseline for our benchmark dataset. The dataset and the evaluation script will be publicly available.
Recommended citation: Jayasinghe, Oshada & Hemachandra, Sahan & Anhettigama, Damith & Kariyawasam, Shenali & Rodrigo, Ranga & Jayasekara, Peshala. (2021). "CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection." Winter Conference on Applications of Computer Vision. 1(1). https://arxiv.org/abs/2110.11867
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Workshop Instructor
Introduction to Robotics Seminar Series, Nalanda College, Colombo 10, Nalanda College, Colombo 10, 2018
I co-initiated the Introduction to Robotics Seminar Series at Nalanda College in collaboration with Robotics Society, Nalanda College, Colombo 10. Topics covered in the seminar included, sensors, actuators and control, working with Microbit boards, practical robot building.
Workshop Instructor
Workshop on Light Field Processing using Low-Complexity Signal Processing Algorithms and Deep Learning, University of Moratuwa, 2021
I worked as one of the instructors of the workshop conducted by Dr. Chamira Edussooriya in IEEE EMBS ISC 2021 at University of Moratuwa. You can find the details of the workshop here.
Workshop Instructor
Beginners’ Workshop of Pi-Mora, Department of Electronic and Telecommunication Engineering, University of Moratuwa, 2021
Conducted the Beginners’ Workshop of Pi-Mora, related to REST API development, and machine learning in Raspberry Pi devices.