Target detection, a crucial component of computer vision, has undergone rapid development in recent years. While existing target detection networks have demonstrated significant improvements in accuracy and speed compared to traditional counterparts, they often come with a drawback: a large number of parameters and high hardware requirements, making them less practical for use on mobile devices. The design of lightweight networks aims to address this challenge by further reducing the number and complexity of model parameters while preserving accuracy. To tackle this issue, we propose a channel pruning method based on the comprehensive evaluation technique of Topsis to trim down the model parameters. During the model fine-tuning stage, we employ a dichotomous pruning fine-tuning strategy to minimize the effort required to restore the model's accuracy.
To reduce the matching ambiguities and explore the matching potential of repetitive features from the scenes with repetitive elements, we present a robust framework of Delaunay triangulation matching based on feature saliency analysis. The feature saliency is computed based on the Gaussian invariance model of the selected reliable features and used as guidance for the triangle-constraint region matching. Due to the seed-sensitive nature of the triangulation matching, the framework integrates the effectiveness validation steps to predict whether the extracted features and the found seeds are proper for matching the specific scenes. By suppressing the surround feature influence, we provide an opportunity for the repetitive features to enhance their feature saliency in the local regions and increase the discriminative power to identify their correspondences. We benchmark the matching performance on the tradeoff between the mean precision and recall. The promising results manifest its effectiveness on the ambiguity reduction.
KEYWORDS: Visualization, 3D modeling, Visual analytics, Detection and tracking algorithms, Visual process modeling, Cameras, RGB color model, Optimization (mathematics), Sensors, 3D acquisition
Visual search is a fundamental technology in the computer vision community. It is difficult to find an object in complex scenes when there exist similar distracters in the background. We propose a target search method in rough three-dimensional-modeling scenes based on a vision salience theory and camera imaging model. We give the definition of salience of objects (or features) and explain the way that salience measurements of objects are calculated. Also, we present one type of search path that guides to the target through salience objects. Along the search path, when the previous objects are localized, the search region of each subsequent object decreases, which is calculated through imaging model and an optimization method. The experimental results indicate that the proposed method is capable of resolving the ambiguities resulting from distracters containing similar visual features with the target, leading to an improvement of search speed by over 50%.
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