scholarly journals Locust Recognition and Detection via Aggregate Channel Features

2019 ◽  
Author(s):  
Dewei Yi ◽  
Jinya Su ◽  
Wen-Hua Chen
Keyword(s):  
Aggregate Channel Features

Exploiting aggregate channel features for urine sediment detection

2018 ◽  
Vol 78 (17) ◽  
pp. 23883-23895 ◽  
Author(s):  
Qiming Sun ◽  
Sen Yang ◽  
Changyin Sun ◽  
Wankou Yang
Keyword(s):  
Urine Sediment ◽  
Aggregate Channel Features

scholarly journals Effectiveness of Human Detection from Aerial Images Taken from Different Heights

TEM Journal ◽  
2021 ◽  
pp. 522-530
Author(s):  
Muhammad Shahir Hakimy Salem ◽  
Fadhlan Hafizhelmi Kamaru Zaman
Keyword(s):  
Model Test ◽  
Search And Rescue ◽  
Human Detection ◽  
Training Data ◽  
Aerial Images ◽  
Single Model ◽  
Bounding Box ◽  
Aggregate Channel Features

Recently, drones have been regularly used to aid in search and rescue in places where it is normally to carry out some of the early forensic victim localization. There are many suitable human detectors for drone use, such as Histogram Oriented Gradient (HOG), You Only Looks Once (YOLO), and Aggregate Channel Features (ACF). In this paper, the height of the aerial images is analyzed for its effect on the accuracy of the detection. This works compares ACF, YOLO MobileNet, and YOLO ResNet50 using a different set of aerial images varying at 10m, 20m, and 30m heights. The results show that in a single-model test, with our proposed bounding-box standardization, YOLO MobileNet achieves significant increase in test precision (AP), with 0.7 AP recorded. For single-model test, YOLO MobileNet yield best AP using 20m training data where it obtained AP of 0.88 (10m test height), 0.82 (20m test height), and 0.91 (30m test height).

scholarly journals AN INVESTIGATION OF AGGREGATE CHANNEL FEATURES OBJECT DETECTOR FOR UAS APPLICATION

2019 ◽  
Vol 78 (1) ◽  
Author(s):  
Nataliia Kuzmenko ◽  
Volodymyr Kharchenko ◽  
Ivan Ostroumov
Keyword(s):  
Aggregate Channel Features
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