Fast, Deep Detection and Tracking of Birds and Nests

Wang, Qiaosong; Rasmussen, Christopher; Song, Chunbo

doi:10.1007/978-3-319-50835-1_14

Cited by 11 publications

(20 citation statements)

References 19 publications

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“…Furthermore, authors adapted and then tested "YOLO"-a CNN-based open-source object detection and classification platform-on real-time video feed obtained from a UAV during flight. The "YOLO" has been proven to be more efficient compared to the traditionally employed machine learning algorithms [9], while it was comparable in terms of detection and classification accuracy (84%), making it the ideal candidate for UAV autonomous flight applications. To put that into perspective, "YOLO" is capable of running networks on video feeds at 150 frames per second.…”

Section: Discussionmentioning

confidence: 99%

“…The positive prediction value for our CNN was calculated to be 99.6%, false discovery rate was 0.4%, true positive rate was 97.4%, and false negative rate was 2.6%. More detailed analysis of "YOLO" performance and its comparison to other CNN algorithms was conducted by Wang et al [9]. an airplane but is not recognized by the network (Figure 4a), or if there is no airplane in the image but one is labeled by the network as being present in the image (Figure 4.b).…”

Section: Neural Network Validationmentioning

confidence: 99%

Section: Neural Network Validationmentioning

confidence: 99%

“…One of the proposed solutions is the implementation of a deep learning-based software which uses a convolutional neural network algorithm to track, detect, and classify objects from raw data in real time. In the last few years, deep convolutional neural networks have shown to be a reliable approach for image object detection and classification due to their relatively high accuracy and speed [6][7][8][9]. Furthermore, a CNN algorithm enables UAVs to convert object information from the immediate environment into abstract information that can be interpreted by machines without human interference.…”

Section: Introductionmentioning

confidence: 99%

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Object Recognition in Aerial Images Using Convolutional Neural Networks

2017

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Abstract:There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network's training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the "YOLO" ("You Only Look Once") platform, objects can be tracked, detected ("seen"), and classified ("comprehended") from video feeds supplied by UAVs in real-time.

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Section: Discussionmentioning

confidence: 99%

Section: Neural Network Validationmentioning

confidence: 99%

Section: Neural Network Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Object Recognition in Aerial Images Using Convolutional Neural Networks

2017

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“…Meanwhile, deep learning methods were applied to the detection of birds where they mostly adopt the RCNN [6] to detect birds in various circumstances with a bounding box surrounding each bird individually [2], [8], [21], [24], [25], [29]. However, their works are limited to situations where the birds are very sparse since the accuracy of bounding box detection methods decreases sharply when the objects get crowded.…”

Section: Introductionmentioning

confidence: 99%

Learning of Counting Crowded Birds of Various Scales via Novel Density Activation Maps

Kim

2020

IEEE Access

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The previous counting methods trained by the density map regression scheme fail to precisely count the number of birds in crowded bird images of various scales. This is due to the coarseness of the manually created target density maps. In this paper, we propose a new counting scheme, called DAM counting, which generates our-first-proposed density activation map (DAM). DAM is a CNN perspective density map that has high activation values where the network focuses on for precise counting of birds. The network is trained to autonomously learn where and how much the DAM should be activated so that the sum of all values in the DAM estimates the number of birds. Moreover, our DAM counting scheme incorporates two segmentation regularizers that enable precise counting of birds with various scales and appearance. Our DAM counting scheme can effectively substitute the existing density map regression scheme, bringing in a remarkable increase in the counting accuracy. We also propose the first crowded bird dataset, called CBD-6000, which is very valuable for crowded bird counting research.

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