Abstract
This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.
Highlights
The development of deep convolutional neural networks and their broader application in the field of digital image processing was observed [1], areas well as the development of decision-making processes automation in the power sector
The Analysis Results for All the Applied Convolutional Neural Networks
The IoU parameter describes the relationship between overlap area which is the sum of the area of the region delimited by the network and tagged manually in the picture and union area which is the product of the area of the region delimited by the network and the region hand-tagged in the picture
Summary
The development of deep convolutional neural networks and their broader application in the field of digital image processing was observed [1], areas well as the development of decision-making processes automation in the power sector. Using UAVs (unmanned aerial vehicles) as a platform for obtaining data on power transmission elements is becoming widespread. There are many challenges to overcome when automating the analysis of digital images captured by UAV vehicles. The biggest o problems are difficulties related to the number of images and resulting data labeling problems. This is usually a manual task that requires precision and accuracy to provide the learning algorithms with the most valuable input information. It is possible to filter out unnecessary, redundant, or distorted data
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