Khandaker Mamun Ahmed

Small-object detection plays a critical role in real-time aerial, Unmanned Aerial Vehicle (UAV), and satellite vision systems, as well as many other domains, where hardware constraints and deployment environments demand high accuracy, low latency, and energy efficiency. Although numerous lightweight object detectors have been proposed, there exists a lack of rigorous, cross-platform benchmarks that evaluate these models under consistent conditions for small-object-focused applications. This paper presents a comprehensive comparative evaluation of eight recent lightweight You Look Only Once (YOLO)-based models, including YOLOX-Nano and YOLOv6–v12 variants. Models are trained using a unified pipeline and evaluated on three representative datasets: VisDrone2019, DOTA-v1.5, and xView. Performance is assessed on both cloud GPUs (NVIDIA A100) and an edge AI platform (Jetson Orin Nano), using PyTorch and TensorRT (FP16) runtimes. The evaluation metrics include mAP@0.5, mAP@0.5:0.95, F1-score, latency, FPS, memory footprint, and energy consumption (via VDD_IN telemetry). Experimental results demonstrate that YOLOv8-Nano and YOLOv9-Tiny achieve leading accuracy across datasets, while YOLOv6-Nano and YOLOv10-Nano offer favorable trade-offs in speed and memory. TensorRT optimization yields 3×–5× speedup over PyTorch. Energy draw ranges from 7.6 W to 10.4 W, depending on the model and configuration. Pareto frontiers reveal that no single model dominates across all objectives. The study highlights the importance of multi-objective benchmarking in edge AI and provides practical guidance for selecting detectors based on deployment-specific constraints. The findings support theory-driven and application-oriented advancement of efficient detection architectures for real-time, resource-aware computer vision systems.

This study evaluates the adoption of DevSecOps among small and medium-sized enterprises (SMEs), identifying key challenges, best practices, and future trends. Through a mixed methods approach backed by the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, we analyzed survey data from 405 SME professionals, revealing that while 68% have implemented DevSecOps, adoption is hindered by technical complexity (41 %), resource constraints (35 %), and cultural resistance (38 %). Despite strong leadership prioritization of security (73 %), automation gaps persist, with only 12 % of organizations conducting security scans per commit. Our findings highlight a growing integration of security tools, particularly API security (63 %) and software composition analysis (62 %), although container security adoption remains low (34 %). Looking ahead, SMEs anticipate artificial intelligence and machine learning to significantly influence DevSecOps, under-scoring the need for proactive adoption of AI -driven security enhancements. Based on our findings, this research proposes strategic best practices to enhance CI/CD) pipeline security including automation, leadership-driven security culture, and cross-team collaboration.

This study evaluates the adoption of DevSecOps among small and medium-sized enterprises (SMEs), identifying key challenges, best practices, and future trends. Through a mixed methods approach backed by the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, we analyzed survey data from 405 SME professionals, revealing that while 68% have implemented DevSecOps, adoption is hindered by technical complexity (41%), resource constraints (35%), and cultural resistance (38%). Despite strong leadership prioritization of security (73%), automation gaps persist, with only 12% of organizations conducting security scans per commit. Our findings highlight a growing integration of security tools, particularly API security (63%) and software composition analysis (62%), although container security adoption remains low (34%). Looking ahead, SMEs anticipate artificial intelligence and machine learning to significantly influence DevSecOps, underscoring the need for proactive adoption of AI-driven security enhancements. Based on our findings, this research proposes strategic best practices to enhance CI/CD pipeline security including automation, leadership-driven security culture, and cross-team collaboration.

In the past few years, automatic building detection in aerial images has become an emerging field in computer vision. Detecting the specific types of houses will provide information in urbanization, change detection, and urban monitoring that play increasingly important roles in modern city planning and natural hazard preparedness. In this paper, we demonstrate the effectiveness of detecting various types of houses in aerial imagery using Faster Region-based Convolutional Neural Network (Faster-RCNN). After formulating the dataset and extracting bounding-box information, pre-trained ResNet50 is used to get the feature maps. The fully convolutional Region Proposal Network (RPN) first predicts the bounds and objectness score of objects (in this case house) from the feature maps. Then, the Region of Interest (RoI) pooling layer extracts interested regions to detect objects that are present in the images. To the best of our knowledge, this is the first attempt at detecting houses using Faster R-CNN that has achieved satisfactory results. This experiment opens a new path to conduct and extent the works not only for civil and environmental domain but also other applied science disciplines.

The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic that has infected millions of people causing millions of deaths around the world. Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the standard screening method for COVID-19 detection but it requires specific molecular-biology training. Moreover, the general workflow is difficult e.g. sample collection, processing time, and analysis expertise, etc. Chest radiographic image analysis can be a good alternative screening method that is faster, more efficient, and requires minimal clinical or molecular biology trained laboratory personnel. Early studies have shown that abnormalities on the chest radiographic images are likely to be the consequence of COVID-19 infection. In this study, we propose DeepCOVIDNet, a deep learning based COVID-19 detection model. Our proposed deep-learning model is a multiclass classifier that can distinguish COVID-19, viral pneumonia, bacterial pneumonia, and healthy chest X-ray images. Our proposed model classifies radiographic images into four distinct classes and achieves the accuracy of 89.47% along with a high degree of precision, recall and F1 score. On a different dataset setting (COVID-19, bacterial pneumonia, viral pneumonia) our model achieves the maximum accuracy of 98.25%. We demonstrate generalizability of our proposed method using 5-fold cross validation for COVID-19 vs pneumonia and COVID-19 vs healthy classification that also manifests promising results.