2 research outputs found
Sheep identity recognition, age and weight estimation datasets
Increased interest of scientists, producers and consumers in sheep
identification has been stimulated by the dramatic increase in population and
the urge to increase productivity. The world population is expected to exceed
9.6 million in 2050. For this reason, awareness is raised towards the necessity
of effective livestock production. Sheep is considered as one of the main of
food resources. Most of the research now is directed towards developing real
time applications that facilitate sheep identification for breed management and
gathering related information like weight and age. Weight and age are key
matrices in assessing the effectiveness of production. For this reason, visual
analysis proved recently its significant success over other approaches. Visual
analysis techniques need enough images for testing and study completion. For
this reason, collecting sheep images database is a vital step to fulfill such
objective. We provide here datasets for testing and comparing such algorithms
which are under development. Our collected dataset consists of 416 color images
for different features of sheep in different postures. Images were collected
fifty two sheep at a range of year from three months to six years. For each
sheep, two images were captured for both sides of the body, two images for both
sides of the face, one image from the top view, one image for the hip and one
image for the teeth. The collected images cover different illumination, quality
levels and angle of rotation. The allocated data set can be used to test sheep
identification, weigh estimation, and age detection algorithms. Such algorithms
are crucial for disease management, animal assessment and ownership
An Efficient Pre-processing Method to Eliminate Adversarial Effects
Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated
by imposing subtle perturbations to inputs that lead a model to predict
incorrect outputs. Currently, a large number of researches on defending
adversarial examples pay little attention to the real-world applications,
either with high computational complexity or poor defensive effects. Motivated
by this observation, we develop an efficient preprocessing method to defend
adversarial images. Specifically, before an adversarial example is fed into the
model, we perform two image transformations: WebP compression, which is
utilized to remove the small adversarial noises. Flip operation, which flips
the image once along one side of the image to destroy the specific structure of
adversarial perturbations. Finally, a de-perturbed sample is obtained and can
be correctly classified by DNNs. Experimental results on ImageNet show that our
method outperforms the state-of-the-art defense methods. It can effectively
defend adversarial attacks while ensure only very small accuracy drop on normal
images.Comment: in Chines