Jul 2026· Asian Journal of Research in Computer Science· 0 citations
TL;DR
Findings indicate a trade-off between computational efficiency and predictive performance under the experimental conditions used and show that the fine-tuned VGG16 model consistently outperformed the plain CNN across the principal performance measures.
Abstract
Despite the growing need for object-detection systems, companies, governments, industries, and organisations face significant challenges in implementing them to address real-world problems. Object detection supports applications ranging from quality control and inventory management to robotics, surveillance, autonomous systems, and defect detection. Advances in deep learning have enabled notable progress through architectures such as convolutional neural networks (CNNs). This study examined the performance of two deep-learning architectures for detecting household items under controlled experimental conditions. A dataset of 2,100 images, comprising three balanced classes—bottles, boxes, and cups - was annotated and used in the experiment. The results showed that the fine-tuned VGG16 model consistently outperformed the plain CNN across the principal performance measures. Intersection over Union (IoU) increased from 57.11% for the plain CNN to 98.00% for the fine-tuned model, while loss decreased from 6.40% to 4.21%. The fine-tuned model also produced a smoother and more stable learning curve, indicating more consistent detection performance. However, this improvement required a longer training time: approximately 14,400 seconds for the fine-tuned model compared with 12,240 seconds for the plain CNN. The comparison was conducted using the same dataset, training duration, and evaluation framework for both architectures. These findings indicate a trade-off between computational efficiency and predictive performance under the experimental conditions used.
A comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures is presented, showing that deeper networks provide better representation, while residual connections and compound scaling improve training stability and efficiency.
Riyaz Mohammed· International Journal of App...· 0 citations
Deeper modern networks outperform the older AlexNet by a wide margin on CIFAR-10, and even a relatively compact ResNet can nearly match the accuracy of a much larger VGG16 in far less time.
Small object detection in crowded urban and aerial scenes remains a critical challenge due to limited pixel information and information loss in deep neural networks. This study introduces a novel optimization framework for YOLOv11, specifically engineered for tiny-scale targets by integrating convolutional block attention modules (CBAM), k-means anchor clustering, and an enhanced feature pyramid network (FPN). Evaluated on the TinyPerson and COCO-mini datasets, the YOLOv11-optimized model achieves significant performance breakthroughs, delivering a +7.3% gain in mean average precision (mAP) and a +10.5% increase in recall over the baseline. Notably, the model achieved a recall of 0.072 on the TinyPerson dataset, with double sensitivity of standard YOLOv11. With a high-speed inference rate of 27.3 FPS, this research demonstrates that strategic architectural refinements can drastically improve small object detection reliability without compromising real-time viability on edge devices.
Husna Sarirah Husin, H. Hao, Yuan-Fei Pan et al.· IAES International Journal o...· 0 citations
Over the past few years, object detection has experienced remarkable progress and development, primarily driven by the development of one-stage and two-stage detection algorithms. Among these, Faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO) have achieved notable success due to their strong performance and computational efficiency. Object detection plays a crucial role in various applications, particularly in autonomous driving systems, where accurate detection of pedestrians, vehicles, and road signs is essential for ensuring safety and reliability. This paper conducts a comparative evaluation of YOLO and Faster R-CNN to analyze their performance in autonomous driving environments. The experiments were conducted using the KITTI open-source dataset, which is widely used for benchmarking object detection models. All experiments were performed on an NVIDIA RTX A5000 GPU to ensure efficient computation, with implementations developed using Python version 3.9.13. The experimental findings indicate that YOLO surpasses Faster R-CNN in performance, attaining an accuracy rate of 90%. These findings highlight the effectiveness of YOLO for real-time object detection tasks, making it a suitable and preferred choice for time-sensitive applications such as autonomous driving systems.
Madhura M. Bhosale, Y. Angal· IAES International Journal o...· 0 citations
The COVID-19 pandemic highlighted the importance of face masks as an effective non-pharmaceutical intervention for reducing the transmission of infectious diseases. Monitoring mask compliance in public environments such as hospitals, educational institutions, transportation hubs, and workplaces remains a challenging task when performed manually. Recent advances in computer vision and deep learning have enabled the development of automated face mask detection systems capable of operating in real time. This paper presents a comparative study of three deep learning architectures, namely a Custom Convolutional Neural Network (CNN), VGG16, and MobileNetV2, for face mask detection. The study employs a publicly available dataset containing 12,000 facial images categorized into mask and no-mask classes. Data preprocessing techniques including resizing, normalization, and augmentation were applied to improve model generalization. Experimental results demonstrate that MobileNetV2 outperforms the other architectures, achieving an accuracy of 98.7%, precision of 98.4%, recall of 99.0%, and an AUC-ROC score of 0.99 while maintaining real-time performance. The proposed system was further integrated with OpenCV for live video stream analysis. The findings indicate that lightweight transfer learning models offer an effective and practical solution for real-time face mask detection in resource-constrained environments.
Ruksar Fatima, Shaista Fatima· International Journal of Sci...· 0 citations
While neural networks represent a promising approach for evaluating sensor data to assess damage presence, location and severity, large amounts of data are required for training. However, the generation of experimental data is both labor-intensive and costly. Transfer learning is a well-established method in deep learning that enables the reuse of knowledge from pre-trained models to improve performance and reduce data requirements across various domains such as computer vision and natural language processing. Its application within the field of strain-based structural health monitoring (SHM) has received little attention from currently published literature.
This work develops a resource-efficient transfer learning approach for strain-based SHM. While the Convolutional Neural Network (CNN) model learns relationships between strain distribution and crack geometry in an aluminum beam from Finite Element (FE) data, fine-tuning adapts it to experimental conditions, accounting for factors such as measurement noise, increasing overall accuracy and robustness.
An encoder-decoder CNN (UNet) is initially trained with synthetic data from FE simulations. The model is then fine-tuned based on a smaller experimental Digital Image Correlation (DIC) dataset obtained from an aluminum beam subjected to a four-point bending fatigue test. For this purpose, the encoder part of the CNN is frozen, while parameters of the final layers of the decoder are updated. The approach is validated with respect to its accuracy, robustness and applicability for SHM systems.
The presented approach significantly reduces the experimental data requirements while improving damage detection performance for an aluminum beam under four-point bending. This study demonstrates that transfer learning enables efficient adaptation to real-world conditions, offering a cost-effective and scalable solution for data-driven SHM.
Finja Carlotta Rentzsch Holm, T. Schalm, Jorge Luis Jiménez Aparicio et al.· e-Journal of Nondestructive...· 0 citations
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