Towards Varroa destructor mite detection using a narrow spectra illumination

• URL: http://arxiv.org/abs/2504.06099v1
• Date: Tue, 08 Apr 2025 14:41:42 GMT
• Title: Towards Varroa destructor mite detection using a narrow spectra illumination
• Authors: Samuel Bielik, Simon Bilik,
• Abstract summary: This paper focuses on the development and modification of a beehive monitoring device and Varroa destructor detection on the bees with the help of hyperspectral imagery.<n>The main objectives were to collect a dataset of bees and mites, and propose the computer vision model which can achieve the detection between bees and mites.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on the development and modification of a beehive monitoring device and Varroa destructor detection on the bees with the help of hyperspectral imagery while utilizing a U-net, semantic segmentation architecture, and conventional computer vision methods. The main objectives were to collect a dataset of bees and mites, and propose the computer vision model which can achieve the detection between bees and mites.

Related papers

• Varroa destructor detection on honey bees using hyperspectral imagery [0.0]
  This paper introduces a method to detect parasitic Varroa destructor mites on the body of western honey bee Apis mellifera. The methodology explores unsupervised (K-means++) and recently developed supervised (KF-PLS) methods for parasitic identification. In light of the emergence of custom-band multispectral cameras, the present research outlines a strategy for identifying the specific wavelengths necessary for effective bee-mite separation.
  arXiv  Detail & Related papers  (2024-03-21T12:40:41Z)
• Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder [4.812718493682454]
  This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. An anomaly detection framework computes metrics directly on the input space, enhancing interpretability and anomaly localization. Our framework offers improved interpretability and localization of anomalies, providing valuable insights for monitoring marine ecosystems.
  arXiv  Detail & Related papers  (2023-07-13T12:26:27Z)
• ODAM: Gradient-based instance-specific visual explanations for object detection [51.476702316759635]
  gradient-weighted Object Detector Activation Maps (ODAM) ODAM produces heat maps that show the influence of regions on the detector's decision for each predicted attribute. We propose Odam-NMS, which considers the information of the model's explanation for each prediction to distinguish duplicate detected objects.
  arXiv  Detail & Related papers  (2023-04-13T09:20:26Z)
• BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision [101.36648828734646]
  We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2022-11-18T18:59:48Z)
• Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots [0.07366405857677225]
  A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. We propose a comprehensive deep learning framework for detecting anomalies and changes in a priori unknown environments.
  arXiv  Detail & Related papers  (2022-08-26T09:52:12Z)
• Peripheral Vision Transformer [52.55309200601883]
  We take a biologically inspired approach and explore to model peripheral vision in deep neural networks for visual recognition. We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data. We evaluate the proposed network, dubbed PerViT, on the large-scale ImageNet dataset and systematically investigate the inner workings of the model for machine perception.
  arXiv  Detail & Related papers  (2022-06-14T12:47:47Z)
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
  arXiv  Detail & Related papers  (2022-03-30T11:44:56Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects [0.0]
  ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets.
  arXiv  Detail & Related papers  (2021-04-26T01:13:28Z)
• UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance [20.318367304051176]
  We propose a holistic anomaly detection system using deep neural networks for surveillance of critical infrastructures. First, we present a method for the explicit representation of spatial layouts of objects in bird-view images. Then, we propose a deep neural network architecture for unsupervised anomaly detection (UAV-AdNet) Unlike studies in the literature, we combine GPS and image data to predict abnormal observations.
  arXiv  Detail & Related papers  (2020-11-05T14:26:29Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.