Detection and Counting of Objects in an Image Using MATLAB

Abstract

This paper presents a MATLAB-based approach for automated object detection and counting in digital images using the Image Processing Toolbox. The method employs image acquisition, preprocessing, threshold-based segmentation, and morphological operations to enhance image quality and isolate objects from the background. Connected component analysis is then applied to identify individual objects, while region-based feature extraction is used to obtain properties such as area, centroid, and shape. The total number of detected components provides the object count. The proposed system offers a fast, accurate, and reliable alternative to manual counting and is applicable in areas such as industrial automation, quality control, medical imaging, traffic monitoring, and scientific research.

Introduction

This study presents a MATLAB-based automated object detection and counting system using the YOLOv4 deep learning model. The system is designed to accurately detect, classify, and count multiple objects in digital images, reducing the need for manual inspection. It has applications in traffic monitoring, surveillance, manufacturing, agriculture, and smart-city systems.

The proposed methodology consists of five stages: image input, object detection using a pre-trained YOLOv4 network, extraction of detection information (bounding boxes, labels, and confidence scores), object counting by category and total count, and visualization of annotated results with statistical outputs. MATLAB, along with the Image Processing Toolbox, Computer Vision Toolbox, Deep Learning Toolbox, and YOLOv4 model, provides the development environment.

Experimental evaluation on 20 test images demonstrated strong performance, achieving 95% object detection accuracy, 96% counting accuracy, 94% precision, 93% recall, and an average processing time of 1.8 seconds per image. The system successfully detected vehicles and pedestrians in various lighting conditions, object densities, and viewing angles while maintaining reliable counting performance.

Although minor challenges were observed in crowded scenes, overlapping objects, poor lighting, and small or partially occluded objects, the system remained robust and effective.

Conclusion

This work presented a MATLAB-based Object Detection and Counting System utilizing a YOLO deep-learning framework for automatic object recognition and enumeration. The proposed system successfully detected and classified multiple object categories, including vehicles and pedestrians, while simultaneously maintaining accurate object counts. Experimental evaluation demonstrated that the system achieved high detection accuracy and reliable counting performance across a variety of image conditions. The generated bounding boxes effectively localized detected objects, enabling accurate identification even in scenes containing numerous targets. The object counting module further enhanced the functionality of the system by providing automated quantitative analysis without requiring manual intervention. Although certain challenges were encountered in highly crowded scenes and under unfavorable lighting conditions, the overall performance remained consistent and dependable. The results confirm that the proposed MATLAB implementation is capable of providing an efficient solution for automated object detection and counting applications. Future enhancements may include real-time video processing, advanced tracking algorithms, and deployment on embedded platforms to further improve system scalability and practical usability. The findings demonstrate that deep-learning-based object detection offers a robust and effective approach for intelligent monitoring and automated counting systems

References

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Copyright

Copyright © 2026 Kumar N Krishnamurthy, Prarthana B, Rachitha S Gowda, Trisha G M, Varsha G M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.