O. Imocha Singh

Owen Singh, Sandeep Singh Sengar

Colorectal cancer contributes significantly to cancer-related mortality. Timely identification and elimination of polyps through colonoscopy screening is crucial in order to decrease mortality rates. Accurately detecting polyps in colonoscopy images is difficult because of the differences in characteristics such as size, shape, texture, and similarity to surrounding tissues. Current deep-learning methods often face difficulties in capturing long-range connections necessary for segmentation. This research presents BetterNet, a convolutional neural network (CNN) architecture that combines residual learning and attention methods to enhance the accuracy of polyp segmentation. The primary characteristics encompass (1) a residual decoder architecture that facilitates efficient gradient propagation and integration of multiscale features. (2) channel and spatial attention blocks within the decoder block to concentrate the learning process on the relevant areas of polyp regions. (3) Achieving state-of-the-art performance on polyp segmentation benchmarks while still ensuring computational efficiency. (4) Thorough ablation tests have been conducted to confirm the influence of architectural components. (5) The model code has been made available as open-source for further contribution. Extensive evaluations conducted on datasets such as Kvasir-SEG, CVC ClinicDB, Endoscene, EndoTect, and Kvasir-Sessile demonstrate that BetterNets outperforms current SOTA models in terms of segmentation accuracy by significant margins. The lightweight design enables real-time inference for various applications. BetterNet shows promise in integrating computer-assisted diagnosis techniques to enhance the detection of polyps and the early recognition of cancer. Link to the code: https://github.com/itsOwen/BetterNet

Sandeep Singh Sengar, Abhishek Kumar, Owen Singh

This study presents significant enhancements in human pose estimation using the MediaPipe framework. The research focuses on improving accuracy, computational efficiency, and real-time processing capabilities by comprehensively optimising the underlying algorithms. Novel modifications are introduced that substantially enhance pose estimation accuracy across challenging scenarios, such as dynamic movements and partial occlusions. The improved framework is benchmarked against traditional models, demonstrating considerable precision and computational speed gains. The advancements have wide-ranging applications in augmented reality, sports analytics, and healthcare, enabling more immersive experiences, refined performance analysis, and advanced patient monitoring. The study also explores the integration of these enhancements within mobile and embedded systems, addressing the need for computational efficiency and broader accessibility. The implications of this research set a new benchmark for real-time human pose estimation technologies and pave the way for future innovations in the field. The implementation code for the paper is available at https://github.com/avhixd/Human_pose_estimation.

Sandeep Singh Sengar, Aswin Kumar, Owen Singh

This study presents a novel driver drowsiness detection system that combines deep learning techniques with the OpenCV framework. The system utilises facial landmarks extracted from the driver's face as input to Convolutional Neural Networks trained to recognise drowsiness patterns. The integration of OpenCV enables real-time video processing, making the system suitable for practical implementation. Extensive experiments on a diverse dataset demonstrate high accuracy, sensitivity, and specificity in detecting drowsiness. The proposed system has the potential to enhance road safety by providing timely alerts to prevent accidents caused by driver fatigue. This research contributes to advancing real-time driver monitoring systems and has implications for automotive safety and intelligent transportation systems. The successful application of deep learning techniques in this context opens up new avenues for future research in driver monitoring and vehicle safety. The implementation code for the paper is available at https://github.com/LUFFY7001/Driver-s-Drowsiness-Detection.

T. Romen Singh, Sudipta Roy, O. Imocha Singh et al.

Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground. Thresholding plays a major in binarization of images. Thresholding can be categorized into global thresholding and local thresholding. In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate. In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background. In such cases, binarization with local thresholding is more appropriate. This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation. Normally the local mean computational time depends on the window size. Our technique uses integral sum image as a prior processing to calculate local mean. It does not involve calculations of standard deviations as in other local adaptive techniques. This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques.