.png){kind=icon}
.png){kind=icon}
Return
Task-specific CNN size reduction through content-specific pruning
.png){kind=icon}
OA
AI
DOI：10.3389/frobt.2025.1552068.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
F
IF:
3
Papers:
466
Citations:
6.9K
View more organizations