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Status: Unchecked ×Keyword: Jetson Nano ×Clear all

2 claims from 1 paper

1. Engineering › Industrial Vision Systems and Defect Detection

   DGNet: An Adaptive Lightweight Defect Detection Model for New Energy Vehicle Battery Current Collector

   Yuan, Chen, Tang, Gao and Wu · IEEE Sensors Journal · 2023

   The paper proposes DGNet, a lightweight defect detection model for new energy vehicle battery current collectors, and reports its accuracy, speed and deployment on a Jetson Nano embedded device.

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   1. UncheckedOn the authors' own battery current collector defect dataset, DGNet reaches 91.8% mAP50 with a 4.0M model, 3.7 GFLOPs and 181.8 frames per second.“Experimental results show that DGNet achieves a mean average precision at intersection over union (IoU) threshold 0.5 ($\text {mAP}_{{50}}$) of 91.8% on the self-made BCC surface defect database, with a model size of 4.0M, only 3.7 giga floating-point operations per second (GFLOPs), and frames/s (F…”
   2. Unchecked“Finally, we deployed DGNet on the embedded platform NVIDIA Jetson Nano for real-time detection, achieving a detection time of 0.074 s per image, meeting the accuracy and real-time detection requirements for BCC defect detection tasks in practical industrial…