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

Liu, Mingjie; Wang, Xianhao; Zhou, Aihua; Fu, Xiuyuan; Ma, Yi-Wei; Piao, Chang Hao

doi:10.3390/s20082238

Cited by 239 publications

(116 citation statements)

References 33 publications

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“…Here, MobileNet and Inception v2 [ 45 ] classifiers were used with SSD for feature extraction task [ 46 , 47 , 48 ]. Similarly, darknet19 feature extractor is used in Yolo v2 module [ 49 , 50 , 51 ]. The three detection frameworks are trained with the same lizard and insect image dataset and similar amount of training time.…”

Section: Resultsmentioning

confidence: 99%

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Ramalingam

Mohan

Pookkuttath

et al. 2020

Sensors

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Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

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Section: Resultsmentioning

confidence: 99%

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Ramalingam

Mohan

Pookkuttath

et al. 2020

Sensors

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“…Since there are four detecting layers in the network of our approach, we select 12 clusters (anchor boxes) and three anchor boxes for each detection scale. The sizes of the anchor boxes for the RSOD dataset are as follows: (21,24), (25,31), (33,41), (51,54) The sizes of the corresponding anchor boxes for the RSOD dataset and UCS-AOD dataset are shown in Table 5.…”

Section: Anchor Boxes Of Our Modelmentioning

confidence: 99%

MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

Xu¹,

Wu²

2020

Remote Sensing

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High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.

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“…In order to overcome this issue, standard deep learning methods need to be modified. Modification has been performed on both two stage detector [11,12] and one stage detectors [13][14][15][16][17] by different authors and in different ways.…”

Section: Small Object Detectionmentioning

confidence: 99%

Multimodel Deep Learning for Person Detection in Aerial Images

Vasić

Papić

2020

Electronics

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In this paper, we propose a novel method for person detection in aerial images of nonurban terrain gathered by an Unmanned Aerial Vehicle (UAV), which plays an important role in Search And Rescue (SAR) missions. The UAV in SAR operations contributes significantly due to the ability to survey a larger geographical area from an aerial viewpoint. Because of the high altitude of recording, the object of interest (person) covers a small part of an image (around 0.1%), which makes this task quite challenging. To address this problem, a multimodel deep learning approach is proposed. The solution consists of two different convolutional neural networks in region proposal, as well as in the classification stage. Additionally, contextual information is used in the classification stage in order to improve the detection results. Experimental results tested on the HERIDAL dataset achieved precision of 68.89% and a recall of 94.65%, which is better than current state-of-the-art methods used for person detection in similar scenarios. Consequently, it may be concluded that this approach is suitable for usage as an auxiliary method in real SAR operations.

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

Cited by 239 publications

References 33 publications

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

Multimodel Deep Learning for Person Detection in Aerial Images

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