EXPANSION OF THE FEATURE SPACE IN THE TASK OF SMALL OBJECT DETECTION IN IMAGES
Abstract
One of the current trends in creating early object detection systems is the development of algorithms
for searching and recognizing small objects in images. In the early detection task, it is necessary
to recognize objects at long distances from the place where they were recorded by the camera.
The image in the image of such objects is represented by a small compact group of pixels, which
undergoes spatial and brightness changes from frame to frame. To successfully solve this problem,
real-world target objects must have large physical dimensions. In addition to the physical dimensions
of the object, the image of the object in the image is influenced by a large number of factors: the resolution
of the camera matrix, the focal length of the lens, the photosensitivity of the matrix, etc. The
vector for solving this problem is directed towards convolutional neural networks. However, even
advanced convolutional neural network architectures face challenges in finding and recognizing
small objects in images. This problem is directly related to the effect of overtraining the neural network
model. Retraining of a neural network model can be assessed based on learning curve analysis.
To reduce the likelihood of overfitting, special methods are used, which are united by the term regularization.
However, in recognizing small-sized objects, existing regularization methods are not
enough. The work examines the developed algorithm for preprocessing a sequence of video frames,
which increases the original feature space with a new independent feature of movement in the frame.
The preprocessing algorithm is based on spatiotemporal filtering of a sequence of video frames, the
application of which extends to a wide range of convolutional neural network architectures. To study
the characteristics of accuracy and recognition of convolutional neural networks, datasets of
grayscale images and images with a sign of motion were generated based on the 3D graphics development
environment Unreal Engine 5. The work presents a criterion for the small size of objects in
images. The accuracy characteristics of the test model of the convolutional neural network were
trained and assessed, and the dynamics of the learning curves of the test model were analyzed. The
positive influence of the proposed algorithm for preprocessing a sequence of video frames on the
integral accuracy of detection of small-sized objects is shown.
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