High-Speed Detector For Low-Powered Devices In Aerial Grasping

• URL: http://arxiv.org/abs/2402.14591v2
• Date: Fri, 1 Mar 2024 12:58:43 GMT
• Title: High-Speed Detector For Low-Powered Devices In Aerial Grasping
• Authors: Ashish Kumar, Laxmidhar Behera
• Abstract summary: Fast Fruit Detector (FFD) is a resource-efficient, single-stage, and postprocessing-free object detector. FFD achieves 100FPS@FP32 precision on the latest 10W NVIDIA Jetson-NX embedded device.
• Score: 16.940649285960028
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous aerial harvesting is a highly complex problem because it requires numerous interdisciplinary algorithms to be executed on mini low-powered computing devices. Object detection is one such algorithm that is compute-hungry. In this context, we make the following contributions: (i) Fast Fruit Detector (FFD), a resource-efficient, single-stage, and postprocessing-free object detector based on our novel latent object representation (LOR) module, query assignment, and prediction strategy. FFD achieves 100FPS@FP32 precision on the latest 10W NVIDIA Jetson-NX embedded device while co-existing with other time-critical sub-systems such as control, grasping, SLAM, a major achievement of this work. (ii) a method to generate vast amounts of training data without exhaustive manual labelling of fruit images since they consist of a large number of instances, which increases the labelling cost and time. (iii) an open-source fruit detection dataset having plenty of very small-sized instances that are difficult to detect. Our exhaustive evaluations on our and MinneApple dataset show that FFD, being only a single-scale detector, is more accurate than many representative detectors, e.g. FFD is better than single-scale Faster-RCNN by 10.7AP, multi-scale Faster-RCNN by 2.3AP, and better than latest single-scale YOLO-v8 by 8AP and multi-scale YOLO-v8 by 0.3 while being considerably faster.

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