RCF-ST: RICHER CONVOLUTIONAL FEATURES NETWORK WITH STRUCTURAL TUNING FOR THE EDGE DETECTION ON NATURAL IMAGES

Abstract

Context. The problem of automating of the edge detection on natural images in intelligent systems is considered. The subject of the research is the deep learning convolutional neural networks for edge detection on natural images.
 Objective. The objective of the research is to improve the edge detection performance of natural images by structural tuning the richer convolutional features network architecture.
 Method. In general, the edge detection performance is influenced by a neural network architecture. To automate the design of the network structure in the paper a structural tuning of a neural network is applied. Computational costs of a structural tuning are incomparably less compared with neural architecture search, but a higher qualification of the researcher is required, and the resulting solution will be suboptimal. In this research it is successively applied first a destructive approach and then a constructive approach to structural tuning of the based architecture of the RCF neural network. The constructive approach starts with a simple architecture network. Hidden layers, nodes, and connections are added to expand the network. The destructive approach starts with a complex architecture network. Hidden layers, nodes, and connections are then deleted to contract the network. The structural tuning of the richer convolutional features network includes: (1) reducing the number of convolutional layers; (2) reducing the number of convolutions in convolutional layers; (3) removing at each stage the sigmoid activation function with subsequent calculation of the loss function; (4) addition of the batch normalization layers after convolutional layers; (5) including the ReLU activation functions after the added batch normalization layers. The obtained neural network is named RCF-ST. The initial color images were scaled to the specified size and then inputted in the neural network. The advisability of each of the proposed stages of network structural tuning was reseached by estimating the edge detection performance using the confusion matrix elements and Figure of Merit. The advisability of a structural tuning of the neural network as a whole was estimated by comparing it with methods known from the literature using the Optimal Dataset Scale and Optimal Image Scale.
 Results. The proposed convolutional neural network has been implemented in software and researched for solving the problem of edge detection on natural images. The structural tuning technique may be used for informed design of the neural network architectures for other artificial intelligence problems.
 Conclusions. The obtained RCF-ST network allows to improve the performance of edge detection on natural images. RCF-ST network is characterized by a significantly fewer parameters compared to the RCF network, which makes it possible to reduce the resource consumption of the network. Besides, RCF-ST network ensures the enhancing of the robustness of edge detection on texture background.