Authors: Ashish Kumar, Laxmidhar Behera

Published on: February 22, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2402.14591

Summary

• What is new: The introduction of the Fast Fruit Detector (FFD), a resource-efficient, single-stage object detector with a novel latent object representation module, achieving high accuracy and speed on low-powered devices.
• Why this is important: Object detection for autonomous aerial harvesting is compute-intensive and challenging on mini low-powered computing devices.
• What the research proposes: FFD utilizes a new latent object representation module and a unique query assignment and prediction strategy, alongside a method to generate extensive training data without exhaustive manual labelling.
• Results: FFD outperforms many existing detectors in accuracy, speed (100FPS@FP32), and efficiency on NVIDIA Jetson-NX devices, even on small-sized instances.

Technical Details

Technological frameworks used: nan

Models used: Fast Fruit Detector (FFD), single-stage object detector, object representation (LOR) module, NVIDIA Jetson-NX

Data used: MinneApple dataset, proprietary fruit detection dataset

Potential Impact

Agricultural technology firms, drone manufacturers, and companies specializing in autonomous harvesting or precision agriculture could significantly benefit or be disrupted.

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