Abstract
In image processing, deep learning networks have been continuously developed and are used in many fields. However, most networks do not reflect image continuity. In this paper, we propose a novel bunch-of-keys module connected to the backend of a deep learning network to improve the detector performance on sequential images. This module optimizes existing deep learning networks to detect sequential images without retraining. This procedure reduces time and computing costs, and the average precision improves with a minimal drop in the frames per second. By adopting a sliding window method that uses three consecutive images, the keys are generated by comparing the positions of the detected boxes for each of the images using generalized intersection over union. The two key types perform correcting operations. The rectifying key has the effect of adding or merging undetected bounding boxes in mid-frame. The tracking key has the effect of compensating for bounding boxes lost for no reason in the third frame. The candidate box extracted using each key determines whether to add or merge to the target image in the correction task. This task calculates the complete intersection over union (CIoU) score between candidate boxes and all boxes of the target image and is divided into add or merge cases according to a set of CIoU criterion. As a result of adding or merging the bounding box to the missing object, detection performance was improved up to 3% in terms of the average precision.
Highlights
Detecting objects in images and classifying them has evolved from a method using traditional techniques to a method using deep learning
3) HYPERPARAMETER OPTIMIZATION OF THE BUNCH OF KEYS NETWORK The novel network presented in this paper uses two hyperparameters: the generalized IoU (GIoU) threshold and complete intersection over union (CIoU) criterion Assuming that each parameter unit is set between 0 and 1 and the unit step used is 0.01, 10,000 experiments are necessary to determine the optimized parameter for the dataset and network, which takes too much time
No additional training is required in deep learning, which requires considerable time to train, which is a significant advantage
Summary
Detecting objects in images and classifying them has evolved from a method using traditional techniques to a method using deep learning. After AlexNet, deep learning made rapid progress, and research has been conducted on various topics, such as network structure, image preprocessing, and data augmentation. We propose a bunch-of-keys (BOK) network that can improve performance by combining information extracted from consecutive images without retraining the trained network. The BOK method introduces a key that compares and matches objects detected in successive images and works by attaching it to the back of the existing network as a module. Related Work A trained detector and a comparison method for the detected boxes are needed to generate a key in the sequential images. As many studies exist on comparative methods, we employed these studies in conducting this research
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