AUTONOMOUS QUADCOPTER WITH IMAGE OBJECT DETECTION METHOD AS A SENDER OF ASSISTANCE FOR COVID-19 PATIENTS

2021 ◽  
Vol 1 (10) ◽  
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Image Object Detection ◽  
Image Object

scholarly journals Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

2021 ◽  
Vol 66 (3) ◽  
pp. 2493-2507
Author(s):  
Hyun Kyu Shin ◽  
Si Woon Lee ◽  
Goo Pyo Hong ◽  
Sael Lee ◽  
Sang Hyo Lee ◽  
...  
Keyword(s):  
Object Detection ◽  
Defect Detection ◽  
Detection Method ◽  
Parking Lots ◽  
Detection Model ◽  
Image Object Detection ◽  
Image Object

Aerial Image Object Detection Method Based on Adaptive ClusDet Network

2021 ◽  
Author(s):  
Zhitian Li ◽  
Shanlin Sun ◽  
Yun Li ◽  
Biaohang Sun ◽  
Kai Tian ◽  
...  
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Aerial Image ◽  
Image Object Detection ◽  
Image Object

scholarly journals Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3415 ◽  
Author(s):  
Jinpeng Zhang ◽  
Jinming Zhang ◽  
Shan Yu
Keyword(s):  
Object Detection ◽  
Sampling Method ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Two Stage ◽  
Baseline Model ◽  
Detection Algorithms ◽  
Imbalance Problem ◽  
Image Object Detection ◽  
Image Object

In the image object detection task, a huge number of candidate boxes are generated to match with a relatively very small amount of ground-truth boxes, and through this method the learning samples can be created. But in fact the vast majority of the candidate boxes do not contain valid object instances and should be recognized and rejected during the training and evaluation of the network. This leads to extra high computation burden and a serious imbalance problem between object and none-object samples, thereby impeding the algorithm’s performance. Here we propose a new heuristic sampling method to generate candidate boxes for two-stage detection algorithms. It is generally applicable to the current two-stage detection algorithms to improve their detection performance. Experiments on COCO dataset showed that, relative to the baseline model, this new method could significantly increase the detection accuracy and efficiency.

Image object detection and semantic segmentation based on convolutional neural network

2019 ◽  
Vol 32 (7) ◽  
pp. 1949-1958 ◽  
Author(s):  
Laigang Zhang ◽  
Zhou Sheng ◽  
Yibin Li ◽  
Qun Sun ◽  
Ying Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
Image Object Detection ◽  
Image Object
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