scholarly journals UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2238 ◽  
Author(s):  
Mingjie Liu ◽  
Xianhao Wang ◽  
Anjian Zhou ◽  
Xiuyuan Fu ◽  
Yiwei Ma ◽  
...  
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicle ◽  
Spatial Information ◽  
Detection Method ◽  
Small Scale ◽  
Small Object ◽  
Aerial Vehicle ◽  
Public Dataset ◽  
Small Object Detection ◽  
Level Performance

Object detection, as a fundamental task in computer vision, has been developed enormously, but is still challenging work, especially for Unmanned Aerial Vehicle (UAV) perspective due to small scale of the target. In this study, the authors develop a special detection method for small objects in UAV perspective. Based on YOLOv3, the Resblock in darknet is first optimized by concatenating two ResNet units that have the same width and height. Then, the entire darknet structure is improved by increasing convolution operation at an early layer to enrich spatial information. Both these two optimizations can enlarge the receptive filed. Furthermore, UAV-viewed dataset is collected to UAV perspective or small object detection. An optimized training method is also proposed based on collected UAV-viewed dataset. The experimental results on public dataset and our collected UAV-viewed dataset show distinct performance improvement on small object detection with keeping the same level performance on normal dataset, which means our proposed method adapts to different kinds of conditions.

Spatial-Prior-Guided Attention for Small Object Detection in Overhead Catenary System

2021 ◽  
Author(s):  
Hang Gong ◽  
Shangdong Zheng ◽  
Zebin Wu ◽  
Yang Xu ◽  
Zhihui Wei ◽  
...  
Keyword(s):  
Object Detection ◽  
Network Architecture ◽  
High Speed ◽  
Spatial Information ◽  
Spatial Relations ◽  
Economic Loss ◽  
Small Object ◽  
Feed Forward Network ◽  
Catenary System ◽  
Small Object Detection

The small defects in overhead catenary system (OCS) can result in long time delays, economic loss and even passenger injury. However, OCS images exhibit great variations with complex background and oblique views which pose a great challenge for small defects detection in high-speed rail system. In this paper, we propose the spatial-prior-guided attention for small object detection in OCS with two main advantages: (1) The spatial-prior is proposed to retain the spatial information between small defects and the electric components in OCS. (2) Based on spatial-prior, the spatial-prior-guided attention model (SAM) is designed to highlight useful information in the features and suppress redundant features response. SAM can model the spatial relations progressively and can be integrated with state-of-the-art feed-forward network architecture with end-to-end training fashion. We conduct extensive experiments on both Split pin datasets and PASCAL–VOC datasets and achieve 97.2% and 79.5% mAP values, respectively. All the experiments demonstrate the competitive performance of our method.

scholarly journals An Improved Swin Transformer-Based Model for Remote Sensing Object Detection and Instance Segmentation

2021 ◽  
Vol 13 (23) ◽  
pp. 4779
Author(s):  
Xiangkai Xu ◽  
Zhejun Feng ◽  
Changqing Cao ◽  
Mengyuan Li ◽  
Jin Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Small Scale ◽  
Detection Accuracy ◽  
Small Object ◽  
Research Fields ◽  
Local Perception ◽  
Segmentation Accuracy ◽  
Small Object Detection ◽  
Instance Segmentation

Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transformer (LPSW) backbone to enhance the local perception of the network and to improve the detection accuracy of small-scale objects. We also designed a spatial attention interleaved execution cascade (SAIEC) network framework, which helped to strengthen the segmentation accuracy of the network. Due to the lack of remote sensing mask datasets, the MRS-1800 remote sensing mask dataset was created. Finally, we combined the proposed backbone with the new network framework and conducted experiments on this MRS-1800 dataset. Compared with the Swin transformer, the proposed model improved the mask AP by 1.7%, mask APS by 3.6%, AP by 1.1% and APS by 4.6%, demonstrating its effectiveness and feasibility.

scholarly journals The Implementation Of Target Recognation to Determine Enemy Coordinates Using Unmanned Aerial Vehicle (UAV) GALAK-24 Aircraft Cameras With Object Detection Method

2020 ◽  
Vol 2 (Oktober) ◽  
pp. 20-28
Author(s):  
Mehmek Ali Akza Arsyad ◽  
Isa Mahfudi ◽  
Bambang Purwanto
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Unmanned Aerial Vehicle ◽  
Programming Language ◽  
Detection Method ◽  
Detection System ◽  
Detection Methods ◽  
Aerial Vehicle ◽  
The Military ◽  
Attacks And Defenses

Abstract – In this era of increasingly advance, camera technology to make it easier for the military to carry out attacks and defenses to destroy embattled opponents, for that is requires camera technology that can detect objects at once with the coordinates or position of the object cleary, so as to help troops to maximize attacks and maneuvers in war. This research is expected to develop GALAK-24 aitcraft equipped with enemy detection cameras and at the same  time determine the position of enemy coorninates in real time in helping intelligence on the bettlefield, thus facilitating decision-making in warfare. The detection system uses object Detection methods to detect objects that are on the surface of the land crossed by the aircraft. The workings of this detection camera is to use the phython programming language thats is connected to the PC and connected also to the camera, when the aircraft makes a flight across enemy territory then the camera will capture the entire enemy territory so that there are vehicle object recorded as well, the target will be reported to calculate the enemy’s strength and enemy position. For security prosedures the aircraft will be flown at on altitude of 500 (m) to avoid enemy personnel fire and also reduce noise so as not to be heard by the enemy reporting the condition of enemy territory, enemy forces at the same time and sent to the operator.

A Study of an X-band FMCW Radar for Small Object Detection

2021 ◽  
Author(s):  
Tripop Tongboonsong ◽  
Akkarat Boonpoonga ◽  
Kittisak Phaebua ◽  
Titipong Lertwiriyaprapa ◽  
Lakkhana Bannawat
Keyword(s):  
Object Detection ◽  
Small Object ◽  
Fmcw Radar ◽  
X Band ◽  
Small Object Detection
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