arXiv subjects

Mohsin Kamal

Publications and source records attributed to Mohsin Kamal.

3 recordsLinked to original sources

arXiv2026

EGRNet: A Lightweight Semantic Segmentation Network with Edge-Gated Refinement and Adversarial Sensing

As autonomous systems and smart cities continue to evolve, the demand for efficient and robust scene understanding becomes increasingly critical. Semantic segmentation plays a key role in enabling autonomous vehicles to comprehend complex urban environments. However, achieving high accuracy with minimal computational cost remains a significant challenge. In this paper, we present Edge-Gated Refinement Network (EGRNet), a lightweight and efficient deep learning model designed for real-time semantic segmentation in urban scenarios. The model incorporates depthwise separable convolutions to reduce computational complexity and dilated residual blocks for capturing rich multi-scale contextual information. Additionally, we introduce a novel Edge-Gated Refinement (EGR) module, which adaptively fuses original and refined features through a learnable gating mechanism, enhancing boundary preservation and edge-sensitive regions. To further improve feature representation, Squeeze-and-Excitation (SE) attention is applied across the network. With only 0.46M parameters, EGRNet achieves state-of-the-art performance while maintaining low computational overhead. When evaluated on the Cityscapes dataset, the model attains a mean Intersection over Union (mIoU) of 65.28%, demonstrating strong accuracy with minimal resource consumption. Moreover, we introduce a lightweight adversarial attack detection strategy, ensuring robustness against adversarial inputs without compromising real-time performance. By combining efficiency, accuracy, and resilience, EGRNet is well-suited for deployment on edge devices in safety-critical real-time applications.

cs.CV↗

arXiv2025

Small Dents, Big Impact: A Dataset and Deep Learning Approach for Vehicle Dent Detection

Conventional car damage inspection techniques are labor-intensive, manual, and frequently overlook tiny surface imperfections like microscopic dents. Machine learning provides an innovative solution to the increasing demand for quicker and more precise inspection methods. The paper uses the YOLOv8 object recognition framework to provide a deep learning-based solution for automatically detecting microscopic surface flaws, notably tiny dents, on car exteriors. Traditional automotive damage inspection procedures are manual, time-consuming, and frequently unreliable at detecting tiny flaws. To solve this, a bespoke dataset containing annotated photos of car surfaces under various lighting circumstances, angles, and textures was created. To improve robustness, the YOLOv8m model and its customized variants, YOLOv8m-t4 and YOLOv8m-t42, were trained employing real-time data augmentation approaches. Experimental results show that the technique has excellent detection accuracy and low inference latency, making it suited for real-time applications such as automated insurance evaluations and automobile inspections. Evaluation parameters such as mean Average Precision (mAP), precision, recall, and F1-score verified the model's efficacy. With a precision of 0.86, recall of 0.84, and F1-score of 0.85, the YOLOv8m-t42 model outperformed the YOLOv8m-t4 model (precision: 0.81, recall: 0.79, F1-score: 0.80) in identifying microscopic surface defects. With a little reduced mAP@0.5:0.95 of 0.20, the mAP@0.5 for YOLOv8m-t42 stabilized at 0.60. Furthermore, YOLOv8m-t42's PR curve area was 0.88, suggesting more consistent performance than YOLOv8m-t4 (0.82). YOLOv8m-t42 has greater accuracy and is more appropriate for practical dent detection applications, even though its convergence is slower.

cs.CV↗

arXiv2020

IoT meets COVID-19: Status, Challenges, and Opportunities

Due to the global pandemic of COVID-19, there is an urgent need to utilize existing technologies to their full potential. Internet of Things (IoT) is regarded as one of the most trending technologies with a great potential in fighting against the coronavirus outbreak. The IoT comprises of a scarce network in which the IoT devices sense the environment and send useful data on the Internet. In this paper, we examine the current status of IoT applications related to COVID-19, identify their deployment and operational challenges, and suggest possible opportunities to further contain the pandemic. Furthermore, we perform analysis for implementing IoT in which internal and external factors are discussed.

cs.DC↗