Asian Journal of Research in Computer Science

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.