Computerized detection of colonic polyps remains an unsolved issue because of the wide variation in the appearance, texture, color, size, and presence of the multiple polyp-like imitators during colonoscopy. In this paper, we propose a deep convoluti
onal neural network based model for the computerized detection of polyps within colonoscopy images. The proposed model comprises 16 convolutional layers with 2 fully connected layers, and a Softmax layer, where we implement a unique approach using different convolutional kernels within the same hidden layer for deeper feature extraction. We applied two different activation functions, MISH and rectified linear unit activation functions for deeper propagation of information and self regularized smooth non-monotonicity. Furthermore, we used a generalized intersection of union, thus overcoming issues such as scale invariance, rotation, and shape. Data augmentation techniques such as photometric and geometric distortions are adapted to overcome the obstacles faced in polyp detection. Detailed benchmarked results are provided, showing better performance in terms of precision, sensitivity, F1- score, F2- score, and dice-coefficient, thus proving the efficacy of the proposed model.
Advancements in artificial intelligence (AI) gives a great opportunity to develop an autonomous devices. The contribution of this work is an improved convolutional neural network (CNN) model and its implementation for the detection of road cracks, po
tholes, and yellow lane in the road. The purpose of yellow lane detection and tracking is to realize autonomous navigation of unmanned aerial vehicle (UAV) by following yellow lane while detecting and reporting the road cracks and potholes to the server through WIFI or 5G medium. The fabrication of own data set is a hectic and time-consuming task. The data set is created, labeled and trained using default and an improved model. The performance of both these models is benchmarked with respect to accuracy, mean average precision (mAP) and detection time. In the testing phase, it was observed that the performance of the improved model is better in respect of accuracy and mAP. The improved model is implemented in UAV using the robot operating system for the autonomous detection of potholes and cracks in roads via UAV front camera vision in real-time.
High frame rates have been known to enhance the perceived visual quality of specific video content. However, the lack of investigation of high frame rates has restricted the expansion of this research field particularly in the context of full-high-de
finition (FHD) and 4K ultra-high-definition video formats. This study involves a subjective and objective quality assessment of compressed FHD videos. First, we compress the FHD videos by employing high-efficiency video coding, and VP9 at five quantization parameter levels for multiple frame rates, i.e., 15fps, 30fps, and 60fps. The FHD videos are obtained from a high frame-rate video database BVI-HFR, spanning various scenes, colors, and motions, and are shown to be representative of the BBC broadcast content. Second, a detailed subjective quality assessment of compressed videos for both encoders and individual frame rates is conducted, resulting in subjective measurements in the form of the differential mean opinion score reflecting the quality of experience. In particular, the aim is to investigate the impact of compression on the perceptual quality of compressed FHD videos and compare the performance of both encoders for each frame rate. Finally, 11 state-of-the-art objective quality assessment metrics are benchmarked using the subjective measurements, to investigate the correlation as a statistical evaluation between the two models in terms of correlation coefficients. A recommendation for enhancing the quality estimation of full-reference (FR) video quality measurements (VQMs) is presented after the extensive investigation.
