Detection of Porcine Huddling Behaviour Based on Improved Multi-view SSD

Zhang, Sunan; Tian, Jianyan; Zhai, Xinpeng; Ji, Zhengxiong

doi:10.1109/cac51589.2020.9327887

Cited by 2 publications

(1 citation statement)

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“…The confidence loss (L con f idence ) occurs when attempting to predict a class, which is softmax loss over multiple classes confidences. Localization loss (L localization ) is defined as a mismatch between the ground truth and the intended boundary boxes [24,47], where α is the weight coefficient, expressed as:…”

Section: Object Detection Frameworkmentioning

confidence: 99%

Deep-Learning-Incorporated Augmented Reality Application for Engineering Lab Training

et al. 2022

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Deep learning (DL) algorithms have achieved significantly high performance in object detection tasks. At the same time, augmented reality (AR) techniques are transforming the ways that we work and connect with people. With the increasing popularity of online and hybrid learning, we propose a new framework for improving students’ learning experiences with electrical engineering lab equipment by incorporating the abovementioned technologies. The DL powered automatic object detection component integrated into the AR application is designed to recognize equipment such as multimeter, oscilloscope, wave generator, and power supply. A deep neural network model, namely MobileNet-SSD v2, is implemented for equipment detection using TensorFlow’s object detection API. When a piece of equipment is detected, the corresponding AR-based tutorial will be displayed on the screen. The mean average precision (mAP) of the developed equipment detection model is 81.4%, while the average recall of the model is 85.3%. Furthermore, to demonstrate practical application of the proposed framework, we develop a multimeter tutorial where virtual models are superimposed on real multimeters. The tutorial includes images and web links as well to help users learn more effectively. The Unity3D game engine is used as the primary development tool for this tutorial to integrate DL and AR frameworks and create immersive scenarios. The proposed framework can be a useful foundation for AR and machine-learning-based frameworks for industrial and educational training.

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Section: Object Detection Frameworkmentioning

confidence: 99%