48,589 research outputs found
A Study on Fish Classification Techniques using Convolutional Neural Networks on Highly Challenged Underwater Images
Underwater Fish Species Recognition (UFSR) has attained significance because of evolving research in underwater life. Manual techniques to distinguish fish can be tricky and tedious. They might require enormous inspecting endeavours, but they can be costly. It results in limited data and a lack of human resources, which may cause incorrect object identification. Automating the fish species detection and recognition utilizing technology would assist sea life science to evolve further. UFSR in wild natural habitats is difficult because the images open natural habitat, complex background, and low luminance. Species Visualization can assist us with deep knowledge of the movements of the species underwater. Automation systems can help to classify the fish accurately and consistently. Image classification has been emerging research with the advancement of deep learning systems. The reason is that the convolutional neural networks (CNNs) don't require explicit feature extraction methods. The vast majority of the current object detection and recognition mechanisms are based on images in the outdoor environment. This paper mainly reviews the strategies proposed in the past years for underwater fish detection and classification. Further, the paper also presents the classification of three different underwater datasets using CNN with evaluation metrics
Weakly Supervised Localization using Deep Feature Maps
Object localization is an important computer vision problem with a variety of
applications. The lack of large scale object-level annotations and the relative
abundance of image-level labels makes a compelling case for weak supervision in
the object localization task. Deep Convolutional Neural Networks are a class of
state-of-the-art methods for the related problem of object recognition. In this
paper, we describe a novel object localization algorithm which uses
classification networks trained on only image labels. This weakly supervised
method leverages local spatial and semantic patterns captured in the
convolutional layers of classification networks. We propose an efficient beam
search based approach to detect and localize multiple objects in images. The
proposed method significantly outperforms the state-of-the-art in standard
object localization data-sets with a 8 point increase in mAP scores
Flow-Guided Feature Aggregation for Video Object Detection
Extending state-of-the-art object detectors from image to video is
challenging. The accuracy of detection suffers from degenerated object
appearances in videos, e.g., motion blur, video defocus, rare poses, etc.
Existing work attempts to exploit temporal information on box level, but such
methods are not trained end-to-end. We present flow-guided feature aggregation,
an accurate and end-to-end learning framework for video object detection. It
leverages temporal coherence on feature level instead. It improves the
per-frame features by aggregation of nearby features along the motion paths,
and thus improves the video recognition accuracy. Our method significantly
improves upon strong single-frame baselines in ImageNet VID, especially for
more challenging fast moving objects. Our framework is principled, and on par
with the best engineered systems winning the ImageNet VID challenges 2016,
without additional bells-and-whistles. The proposed method, together with Deep
Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The
code is available at
https://github.com/msracver/Flow-Guided-Feature-Aggregation
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