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This paper explores the problem of deploying machine learning (ML)-based object detection and segmentation models on edge platforms to enable realtime caveline detection for Autonomous Underwater Vehicles (AUVs) used for under-water cave exploration and mapping. We specifically investigate three ML models, i.e., U-Net, Vision Transformer (ViT), and YOLOv8, deployed on three edge platforms: Raspberry Pi-4, Intel Neural Compute Stick 2 (NCS2), and NVIDIA Jetson Nano. The experimental results unveil clear tradeoffs between model accuracy, processing speed, and energy consumption. The most accurate model has shown to be U-Net with an 85.53 F1-score and 85.38 Intersection Over Union (IoU) value. Meanwhile, the highest inference speed and lowest energy consumption are achieved by the YOLOv8 model deployed on Jetson Nano operating in the high-power and low-power modes, respectively. The comprehensive quantitative analyses and comparative results provided in the paper highlight important nuances that can guide the deployment of caveline detection systems on underwater robots for ensuring safe and reliable AUV navigation during underwater cave exploration and mapping missions.
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Battery-Free Camera Occupancy Detection System
Occupancy detection systems are commonly equipped with high quality cameras and a processor with high computational power to run detection algorithms. This paper presents a human occupancy detection system that uses battery-free cameras and a deep learning model implemented on a low-cost hub to detect human presence. Our low-resolution camera harvests energy from ambient light and transmits data to the hub using backscatter communication. We implement the state-of-the-art YOLOv5 network detection algorithm that offers high detection accuracy and fast inferencing speed on a Raspberry Pi 4 Model B. We achieve an inferencing speed of ∼100ms per image and an overall detection accuracy of >90% with only 2GB CPU RAM on the Raspberry Pi. In the experimental results, we also demonstrate that the detection is robust to noise, illuminance, occlusion, and angle of depression.
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- Award ID(s):
- 1823148
- PAR ID:
- 10303866
- Date Published:
- Journal Name:
- EMDL 2021: 5th International Workshop on Embedded and Mobile Deep Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3469116.3470013
