Vehicle detection from UAV imagery with deep learning: A review
Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important
tasks in a large number of computer vision-based applications. This crucial task needed to …
tasks in a large number of computer vision-based applications. This crucial task needed to …
Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer
Controlling weed infestation through chemicals (herbicides & pesticides) is essential for crop
yield. However, excessive use of these chemicals has caused severe agronomic and …
yield. However, excessive use of these chemicals has caused severe agronomic and …
Real-time pattern-recognition of GPR images with YOLO v3 implemented by tensorflow
Artificial intelligence (AI) is widely used in pattern recognition and positioning. In most of the
geological exploration applications, it needs to locate and identify underground objects …
geological exploration applications, it needs to locate and identify underground objects …
SI‐EDTL: swarm intelligence ensemble deep transfer learning for multiple vehicle detection in UAV images
Z Ghasemi Darehnaei, M Shokouhifar… - Concurrency and …, 2022 - Wiley Online Library
This article proposes a swarm intelligence ensemble deep transfer learning (named SI‐
EDTL) for multiple vehicle detection in unmanned aerial vehicle (UAV) images. This method …
EDTL) for multiple vehicle detection in unmanned aerial vehicle (UAV) images. This method …
Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery
Volunteer cotton (VC) plants growing in the fields of inter-seasonal and rotated crops, like
corn, can serve as hosts to boll weevil pests once they reach pin-head square stage (5–6 …
corn, can serve as hosts to boll weevil pests once they reach pin-head square stage (5–6 …
Deepbrain: Experimental evaluation of cloud-based computation offloading and edge computing in the internet-of-drones for deep learning applications
Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data
for various Internet-of-Things (IoT)/smart cities applications such as search and rescue …
for various Internet-of-Things (IoT)/smart cities applications such as search and rescue …
Data-efficient domain adaptation for semantic segmentation of aerial imagery using generative adversarial networks
Despite the significant advances noted in semantic segmentation of aerial imagery, a
considerable limitation is blocking its adoption in real cases. If we test a segmentation model …
considerable limitation is blocking its adoption in real cases. If we test a segmentation model …
An accurate car counting in aerial images based on convolutional neural networks
This paper proposes a simple and effective single-shot detector model to detect and count
cars in aerial images. The proposed model, called heatmap learner convolutional neural …
cars in aerial images. The proposed model, called heatmap learner convolutional neural …
Adversarial examples for vehicle detection with projection transformation
J Cui, W Guo, H Huang, X Lv, H Cao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV) imaging object detection systems based on deep neural
networks are vulnerable to adversarial patch attacks. However, existing UAV image …
networks are vulnerable to adversarial patch attacks. However, existing UAV image …
Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination
FS Hass, J Jokar Arsanjani - ISPRS International Journal of Geo …, 2020 - mdpi.com
Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with
capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night …
capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night …