3,633 research outputs found
DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels
The impact of soiling on solar panels is an important and well-studied
problem in renewable energy sector. In this paper, we present the first
convolutional neural network (CNN) based approach for solar panel soiling and
defect analysis. Our approach takes an RGB image of solar panel and
environmental factors as inputs to predict power loss, soiling localization,
and soiling type. In computer vision, localization is a complex task which
typically requires manually labeled training data such as bounding boxes or
segmentation masks. Our proposed approach consists of specialized four stages
which completely avoids localization ground truth and only needs panel images
with power loss labels for training. The region of impact area obtained from
the predicted localization masks are classified into soiling types using the
webly supervised learning. For improving localization capabilities of CNNs, we
introduce a novel bi-directional input-aware fusion (BiDIAF) block that
reinforces the input at different levels of CNN to learn input-specific feature
maps. Our empirical study shows that BiDIAF improves the power loss prediction
accuracy by about 3% and localization accuracy by about 4%. Our end-to-end
model yields further improvement of about 24% on localization when learned in a
weakly supervised manner. Our approach is generalizable and showed promising
results on web crawled solar panel images. Our system has a frame rate of 22
fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected
first of it's kind dataset for solar panel image analysis consisting 45,000+
images.Comment: Accepted for publication at WACV 201
A Review of Object Detection Models based on Convolutional Neural Network
Convolutional Neural Network (CNN) has become the state-of-the-art for object
detection in image task. In this chapter, we have explained different
state-of-the-art CNN based object detection models. We have made this review
with categorization those detection models according to two different
approaches: two-stage approach and one-stage approach. Through this chapter, it
has shown advancements in object detection models from R-CNN to latest
RefineDet. It has also discussed the model description and training details of
each model. Here, we have also drawn a comparison among those models.Comment: 17 pages, 11 figures, 1 tabl
Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
Scene parsing, or semantic segmentation, consists in labeling each pixel in
an image with the category of the object it belongs to. It is a challenging
task that involves the simultaneous detection, segmentation and recognition of
all the objects in the image.
The scene parsing method proposed here starts by computing a tree of segments
from a graph of pixel dissimilarities. Simultaneously, a set of dense feature
vectors is computed which encodes regions of multiple sizes centered on each
pixel. The feature extractor is a multiscale convolutional network trained from
raw pixels. The feature vectors associated with the segments covered by each
node in the tree are aggregated and fed to a classifier which produces an
estimate of the distribution of object categories contained in the segment. A
subset of tree nodes that cover the image are then selected so as to maximize
the average "purity" of the class distributions, hence maximizing the overall
likelihood that each segment will contain a single object. The convolutional
network feature extractor is trained end-to-end from raw pixels, alleviating
the need for engineered features. After training, the system is parameter free.
The system yields record accuracies on the Stanford Background Dataset (8
classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170
classes) while being an order of magnitude faster than competing approaches,
producing a 320 \times 240 image labeling in less than 1 second.Comment: 9 pages, 4 figures - Published in 29th International Conference on
Machine Learning (ICML 2012), Jun 2012, Edinburgh, United Kingdo
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