UCA-YOLOv8n: a real-time and efficient fruit chunks detection algorithm for meal-assistance robot
Fei Liu, Mingyue Hu
TL;DR
This paper introduces UCA-YOLOv8n, a fast and accurate algorithm for detecting fruit chunks in meal-assistance robots.
Contribution
The novel integration of the UIB module, CA mechanism, and ADown module improves detection accuracy while reducing model size.
Findings
UCA-YOLOv8n reduces model size by 1.9 MB and parameter count by 2.5 GFLOPs.
The algorithm improves mAP50 and mAP50-95 by 2.1% and 3.3%, respectively.
UCA-YOLOv8n outperforms other mainstream detection algorithms in accuracy and efficiency.
Abstract
The advancement of assistive technologies for individuals with disabilities has increased the demand for efficient and accurate object detection algorithms, particularly in meal-assistance robots designed to identify and handle food items such as fruit chunks. However, existing algorithms for fruit chunk detection often suffer from prolonged inference times and insufficient accuracy. We propose an improved YOLOv8n algorithm optimized for real-time, high-accuracy fruit chunk detection. The Universal Inverted Bottleneck (UIB) module has been integrated into the original C2f structure, significantly reducing the model’s parameter count while preserving detection accuracy. Furthermore, the coordinate attention (CA) mechanism has been incorporated into the detection head to enhance the focus on fruit chunk regions within complex backgrounds while suppressing irrelevant features, thus…
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Taxonomy
TopicsAdvanced Neural Network Applications · Smart Agriculture and AI · IoT and Edge/Fog Computing