5,590 research outputs found
Region based object detectors for recognizing birds in aerial images
This project explores different types of deep neural networks (DNNs) for recognizing birds in aerial images based on real data provided by the Missouri Department of Conservation. The pipeline to identify birds from an image consist of two phases. First, region proposals are created by a DNN, where each region proposal is a sub-area of the image that possibly contains a bird. Second, a DNN is trained as a bird classifier using these region proposals. The bird detection performance is evaluated using the Precision, Recall and F1 scores on a separate test dataset. For the region proposal phase, a Region Proposal Network (RPN) has been implemented and tested, obtaining a Recall above 0.99, which means that the region proposal boxes cover almost all the birds. For the classification phase, a modification of Fast Region-based Convolutional Neural Network (Fast RCNN), a simple Convolutional Neural Network (CNN), and a Capsule Network, have been implemented and tested. For all of them, different hyper-parameters have been explored to increase the final F1 score. These models have been evaluated using two bird dataset variants: easy (with simple backgrounds) and hard (with complex background). Experimental results show that birds can be effectively recognized using the DNNs, especially in the easy dataset. Fast RCNN with a backbone architecture of ResNet50 and in conjunction with other techniques like Feature Pyramids Networks achieved the best results, with a maximum F1 score of 0.902. Simple CNN and Capsule Network achieved a score slightly above 0.8. The techniques used, datasets and results are analyzed to find the main causes of failures in some situations.by Pedro Jesus Carrion CastagnolaIncludes bibliographical reference
Fast Recognition of birds in offshore wind farms based on an improved deep learning model
The safety of wind turbines is a prerequisite for the stable operation of
offshore wind farms. However, bird damage poses a direct threat to the safe
operation of wind turbines and wind turbine blades. In addition, millions of
birds are killed by wind turbines every year. In order to protect the
ecological environment and maintain the safe operation of offshore wind
turbines, and to address the problem of the low detection capability of current
target detection algorithms in low-light environments such as at night, this
paper proposes a method to improve the network performance by integrating the
CBAM attention mechanism and the RetinexNet network into YOLOv5. First, the
training set images are fed into the YOLOv5 network with integrated CBAM
attention module for training, and the optimal weight model is stored. Then,
low-light images are enhanced and denoised using Decom-Net and Enhance-Net, and
the accuracy is tested on the optimal weight model. In addition, the k-means++
clustering algorithm is used to optimise the anchor box selection method, which
solves the problem of unstable initial centroids and achieves better clustering
results. Experimental results show that the accuracy of this model in bird
detection tasks can reach 87.40%, an increase of 21.25%. The model can detect
birds near wind turbines in real time and shows strong stability in night,
rainy and shaky conditions, proving that the model can ensure the safe and
stable operation of wind turbines
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
Small Object Detection (SOD) is an important machine vision topic because (i)
a variety of real-world applications require object detection for distant
objects and (ii) SOD is a challenging task due to the noisy, blurred, and
less-informative image appearances of small objects. This paper proposes a new
SOD dataset consisting of 39,070 images including 137,121 bird instances, which
is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The
detail of the challenge with the SOD4SB dataset is introduced in this paper. In
total, 223 participants joined this challenge. This paper briefly introduces
the award-winning methods. The dataset, the baseline code, and the website for
evaluation on the public testset are publicly available.Comment: This paper is included in the proceedings of the 18th International
Conference on Machine Vision Applications (MVA2023). It will be officially
published at a later date. Project page :
https://www.mva-org.jp/mva2023/challeng
Weed Recognition in Agriculture: A Mask R-CNN Approach
Recent interdisciplinary collaboration on deep learning has led to a growing interest in its application in the agriculture domain. Weed control and management are some of the crucial tasks in agriculture to maintain high crop productivity. The inception phase of weed control and management is to successfully recognize the weed plants, followed by providing a suitable management plan. Due to the complexities in agriculture images, such as similar colour and texture, we need to incorporate a deep neural network that uses pixel-wise grouping for identifying the plant species. In this thesis, we analysed the performance of one of the most popular deep neural networks aimed to solve the instance segmentation (pixel-wise analysis) problems: Mask R-CNN, for weed plant recognition (detection and classification) using field images and aerial images. We have used Mask R-CNN to recognize the crop plants and weed plants using the Crop/Weed Field Image Dataset (CWFID) for the field image study. However, the CWFID\u27s limitations are that it identifies all weed plants as a single class and all of the crop plants are from a single organic carrot field. We have created a synthetic dataset with 80 weed plant species to tackle this problem and tested it with Mask R-CNN to expand our study. Throughout this thesis, we predominantly focused on detecting one specific invasive weed type called Persicaria Perfoliata or Mile-A-Minute (MAM) for our aerial image study. In general, supervised model outcomes are slow to aerial images, primarily due to large image size and scarcity of well-annotated datasets, making it relatively harder to recognize the species from higher altitudes. We propose a three-level (leaves, trees, forest) hierarchy to recognize the species using Unmanned Aerial Vehicles(UAVs) to address this issue. To create a dataset that resembles weed clusters similar to MAM, we have used a localized style transfer technique to transfer the style from the available MAM images to a portion of the aerial images\u27 content using VGG-19 architecture. We have also generated another dataset at a relatively low altitude and tested it with Mask R-CNN and reached ~92% AP50 using these low-altitude resized images
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