scholarly journals Applying automated object detection in archaeological practice: A case study from the southern Netherlands

2021 ◽  
Author(s):  
Wouter B. Verschoof‐van der Vaart ◽  
Karsten Lambers
Keyword(s):  
Object Detection ◽  
Archaeological Practice ◽  
Southern Netherlands ◽  
Automated Object Detection

scholarly journals Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

2021 ◽  
Vol 7 (4) ◽  
pp. 64
Author(s):  
Tanguy Ophoff ◽  
Cédric Gullentops ◽  
Kristof Van Beeck ◽  
Toon Goedemé
Keyword(s):  
Computational Complexity ◽  
Object Detection ◽  
Network Optimization ◽  
Real Life ◽  
Optimization Techniques ◽  
Training Data ◽  
Single Shot ◽  
Standard Object ◽  
Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals A computer vision-based object detection and counting for COVID-19 protocol compliance: a case study of Jakarta

2020 ◽  
Author(s):  
Muhammad Lanang Afkaar Ar ◽  
Sulthan Muzakki Adytia S ◽  
Yudhistira Nugraha ◽  
Farizah Rizka R ◽  
Andy Ernesto ◽  
...  
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Detection And Counting ◽  
Protocol Compliance

scholarly journals Carnavalesk taalgebruik en de constructie van lokale identiteiten

2012 ◽  
Vol 1 (1) ◽  
pp. 15-40 ◽  
Author(s):  
Leonie M.E.A. Cornips ◽  
Vincent de Rooij ◽  
Irene Stengs
Keyword(s):  
Interdisciplinary Approach ◽  
Integrated Analysis ◽  
Cultural Elements ◽  
Language And Culture ◽  
Norms And Values ◽  
Southern Netherlands ◽  
Interdisciplinary Study ◽  
The City ◽  
Selection Of

This article aims to encourage the interdisciplinary study of ‘languaculture,’ an approach to language and culture in which ideology, linguistic and cultural forms, as well as praxis are studied in relation to one another. An integrated analysis of the selection of linguistic and cultural elements provides insight into how these choices arise from internalized norms and values, and how people position themselves toward received categories and hegemonic ideologies. An interdisciplinary approach will stimulate a rethinking of established concepts and methods of research. It will also lead to a mutual strengthening of linguistic, sociolinguistic, and anthropological research. This contribution focuses on Limburg and the linguistic political context of this Southern-Netherlands region where people are strongly aware of their linguistic distinctiveness. The argument of the paper is based on a case study of languaculture, viz. the carnivalesque song ‘Naar Talia’ (To Italy) by the Getske Boys from the city of Heerlen.

Justifying the Importance of Color Cues in Object Detection: A Case Study on Pedestrian

2012 ◽  
pp. 387-397 ◽  
Author(s):  
Qingyuan Wang ◽  
Junbiao Pang ◽  
Lei Qin ◽  
Shuqiang Jiang ◽  
Qingming Huang
Keyword(s):  
Object Detection ◽  
Color Cues

scholarly journals IoT Driven Automated Object Detection Algorithm for Urban Surveillance System in Smart City

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1687-1691
Keyword(s):  
Object Detection ◽  
Road Traffic ◽  
Smart Cities ◽  
Digital Camera ◽  
Detection Algorithm ◽  
License Plate ◽  
Surveillance Systems ◽  
Research Issues ◽  
Automated Object Detection ◽  
Research Challenge

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoTdriven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. The other sensors like flame and ultrasonic sensor are used to monitor nearby objects. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems

An RFID-Based Fallen Object Detection System: A Case Study of Hong Kong’s Light Rail System

2018 ◽  
Vol 2 (2) ◽  
pp. 55-67 ◽  
Author(s):  
C. H. Wu ◽  
G. T. S. Ho ◽  
K. L. Yung ◽  
W. W. Y. Tam ◽  
W. H. Ip
Keyword(s):  
Object Detection ◽  
Detection System ◽  
Light Rail ◽  
System A ◽  
Rail System
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