Mr. Nadeem Yousuf Profile

Nadeem Yousuf

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Proceedings Article | 7 June 2023 Paper

An integral computer vision system for apple detection, classification, and semantic segmentation

Tajamul Ashraf, Naiyer Abbas, Mohammad Haseeb, Nadeem Yousuf, Janib Bashir

Proceedings Volume 12701, 127011H (2023) https://doi.org/10.1117/12.2680881

KEYWORDS: Image segmentation, Semantics, Object detection, Education and training, Classification systems, Performance modeling, Machine learning, Image processing, Computer vision technology, Image classification

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The area of Computer Vision has gone through exponential growth and advancement over the past decade. It is mainly due to the introduction of effective deep-learning methodologies and the availability of massive data. This has resulted in the incorporation of intelligent computer vision schemes to automate the different number of tasks. In this paper, we have worked on similar lines. We have proposed an integrated system for the development of robotic arms, considering the current situation in fruit identification, classification, counting, and generating their masks through semantic segmentation. The current method of manually doing these processes is time-consuming and is not feasible for large fields. Due to this, multiple works have been proposed to automate harvesting tasks to minimize the overall overhead. However, there is a lack of an integrated system that can automate all these processes together. As a result, we are proposing one such approach based on different machine learning techniques. For each process, we propose to use the most effective learning technique with computer vision capability. Thus, proposing an integrated intelligent end-to-end computer vision-based system to detect, classify, count, and identify the apples. In this system, we modified the YOLOv3 algorithm to detect and count the apples effectively. The proposed scheme works even under variable lighting conditions. The system was trained and tested using a standard benchmark i.e., MinneApple. Experimental results show an average accuracy of 91%.

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