Asphalt pavement distresses are the major concern of underdeveloped and developed nations for the smooth running of daily life commute. Among various pavement failures, numerous research can be found on pothole detection as they are injurious to automobiles and passengers that may turn into an accident. This work is intended to explore the potential of deep learning models and deploy three superlative deep learning models on edge devices for pothole detection. In this work, we have exploited the AI kit (OAK‐D) on a single‐board computer (Raspberry Pi) as an edge platform for pothole detection. Detailed real‐time performance comparison of state‐of‐the‐art deep learning models and object detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny‐YOLOv4, YOLOv5, and SSD‐mobilenetv2) for pothole detection is presented. The experimentation is performed on an image dataset with pothole in diverse road conditions and illumination variations as well as on real‐time video captured through a moving vehicle. The Tiny‐YOLOv4, YOLOv4, and YOLOv5 evince the highest mean average precision (mAP) of 80.04%, 85.48%, and 95%, respectively, on the image set, thus proving the strength of the proposed approach for pothole detection and deployed on OAK‐D for real‐time detection. The study corroborated Tiny‐YOLOv4 as the befitted model for real‐time pothole detection with 90% detection accuracy and 31.76 FPS.

Melanoma is the skin cancer caused by the ultraviolet radiation from the Sun and has only 15-20% of survival rate. Late diagnosis of melanoma leads to the severe malignancy of disease, and metastasis expands to the other body organs i.e. liver, lungs and brain. The dermatologists analyze the pigmented lesions over the skin to discriminate melanoma from other skin diseases. However, the imprecise analysis results in the form of a series of biopsies and it complicates the treatment. Meanwhile, the process of melanoma detection can be expedited through computer vision methods by analyzing the dermoscopic images automatically. However, the visual similarity between the normal and infected skin regions, and artifacts like gel bubbles, hair and clinical marks indicate low accuracy rates for these approaches. To overcome these challenges, in this article, a melanoma detection and segmentation approach is presented that brings significant improvement in terms of accuracy against state-of-the-art approaches. As a first step, the artifacts like hairs, gel bubbles, and clinical marks are removed from the dermoscopic images by applying the morphological operations, and image regions are sharpen. Afterwards, for infected region detection, we used YOLOv4 object detector by tuning it for melanoma detection to discriminate the highly correlated infected and non-infected regions. Once the bounding boxes against the melanoma regions are obtained, the infected melanoma regions are extracted by applying the active contour segmentation approach. For performance evaluation, the proposed approach is evaluated on ISIC2018 and ISIC2016 datasets and results are compared against state-of-the-art melanoma detection, and segmentation techniques. Our proposed approach achieves average dice score as 1 and Jaccard coefficient as 0.989. The segmentation result validates the practical bearing of our method in development of clinical decision support system for melanoma diagnosis in contrast to state-of-the-art methods. The YOLOv4 detector is capable to detect multiple skin diseases of same patient and multiple diseases of various patients.

A swarm of robots is the coordination of multiple robots that can perform a collective task and solve a problem more efficiently than a single robot. Over the last decade, this area of research has received significant interest from scientists due to its large field of applications in military or civil, including area exploration, target search and rescue, security and surveillance, agriculture, air defense, area coverage and real-time monitoring, providing wireless services, and delivery of goods. This research domain of collective behaviour draws inspiration from self-organizing systems in nature, such as honey bees, fish schools, social insects, bird flocks, and other social animals. By replicating the same set of interaction rules observed in these natural swarm systems, robot swarms can be created. The deployment of robot swarm or group of intelligent robots in a real-world scenario that can collectively perform a task or solve a problem is still a substantial research challenge. Swarm robots are differentiated from multi-agent robots by specific qualifying criteria, including the presence of at least three agents and the sharing of relative information such as altitude, position, and velocity among all agents. Each agent should be intelligent and follow the same set of interaction rules over the whole network. Also, the system’s stability should not be affected by leaving or disconnecting an agent from a swarm. This survey illustrates swarm systems’ basics and draws some projections from its history to its future. It discusses the important features of swarm robots, simulators, real-world applications, and future ideas.

Human action recognition has emerged as a challenging research domain for video understanding and analysis. Subsequently, extensive research has been conducted to achieve the improved performance for recognition of human actions. Human activity recognition has various real time applications, such as patient monitoring in which patients are being monitored among a group of normal people and then identified based on their abnormal activities. Our goal is to render a multi class abnormal action detection in individuals as well as in groups from video sequences to differentiate multiple abnormal human actions. In this paper, You Look only Once (YOLO) network is utilized as a backbone CNN model. For training the CNN model, we constructed a large dataset of patient videos by labeling each frame with a set of patient actions and the patient’s positions. We retrained the back-bone CNN model with 23,040 labeled images of patient’s actions for 32 epochs. Across each frame, the proposed model allocated a unique confidence score and action label for video sequences by finding the recurrent action label. The present study shows that the accuracy of abnormal action recognition is 96.8%. Our proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques. The results indicate that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring.