Task-specific CNN size reduction through content-specific pruning
Konyrbaev N. Lukac M. Ibadulla S. Diveev A. Sofronova E. Galymzhankyzy A.
2025Frontiers Media SA
Frontiers in Robotics and AI
2025#12
The widespread and growing use of flying unmanned aerial vehicles (UAVs) is attributed to their high spatial mobility, autonomous control, and lower cost compared to usual manned flying vehicles. Applications, such as surveying, searching, or scanning the environment with application-specific sensors, have made extensive use of UAVs in fields like agriculture, geography, forestry, and biology. However, due to the large number of applications and types of UAVs, limited power has to be taken into account when designing task-specific software for a target UAV. In particular, the power constraints of smaller UAVs will generally necessitate reducing power consumption by limiting functionality, decreasing their movement radius, or increasing their level of autonomy. Reducing the overhead of control and decision-making software onboard is one approach to increasing the autonomy of UAVs. Specifically, we can make the onboard control software more efficient and focused on specific tasks, which means it will need less computing power than a general-purpose algorithm. In this work, we focus on reducing the size of the computer vision object classification algorithm. We define different tasks by specifying which objects the UAV must recognize, and we construct a convolutional neural network (CNN) for each specific classification. However, rather than creating a custom CNN that requires its dataset, we begin with a pre-trained general-purpose classifier. We then choose specific groups of objects to recognize, and by using response-based pruning (RBP), we simplify the general-purpose CNN to fit our specific needs. We evaluate the pruned models in various scenarios. The results indicate that the evaluated task-specific pruning can reduce the size of the neural model and increase the accuracy of the classification tasks. For small UAVs intended for tasks with reduced visual content, the proposed method solves both the size reduction and individual model training problems. Copyright
computer vision , image classification , machine learning , neural network pruning , noisy data
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Department of Computer Science, Institute of Engineering and Technology, Korkyt Ata Kyzylorda University, Kyzylorda, Kazakhstan
Department of Computer and Network Engineering, Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan
Federal Research Center Computer Science and Control of the Russian Academy of Sciences (FSI), Moscow, Russian Federation
Applied Informatics and Intelligent Systems in Human Sciences Department, RUDN University, Moscow, Russian Federation
Department of Computer Science
Department of Computer and Network Engineering
Federal Research Center Computer Science and Control of the Russian Academy of Sciences (FSI)
Applied Informatics and Intelligent Systems in Human Sciences Department
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026