Towards Universal Object Detection

Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments

Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

Geospatial object detection of remote sensing imagery has been attracting an increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of previously proposed object detectors are very sensitive to object deformations, such as scaling and rotation. To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB). A Fourier-based rotation-invariant feature is first generated in polar coordinate. Then, the extracted features can be further structurally refined using aggregate channel features. This leads to a faster feature computation and more robust feature representation, which is good fitting for the coming boosting learning. Finally, in the test phase, we achieve a fast pyramid feature extraction by estimating a scale factor instead of directly collecting all features from image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness of the FRIFB compared to previous state-of-the-art methods

A Novel Multiscale Edge Detection Approach Based on Nonsubsampled Contourlet Transform and Edge Tracking

Edge detection is a fundamental task in many computer vision applications. In this paper, we propose a novel multiscale edge detection approach based on the nonsubsampled contourlet transform (NSCT): a fully shift-invariant, multiscale, and multidirection transform. Indeed, unlike traditional wavelets, contourlets have the ability to fully capture directional and other geometrical features for images with edges. Firstly, compute the NSCT of the input image. Secondly, the K-means clustering algorithm is applied to each level of the NSCT for distinguishing noises from edges. Thirdly, we select the edge point candidates of the input image by identifying the NSCT modulus maximum at each scale. Finally, the edge tracking algorithm from coarser to finer is proposed to improve robustness against spurious responses and accuracy in the location of the edges. Experimental results show that the proposed method achieves better edge detection performance compared with the typical methods. Furthermore, the proposed method also works well for noisy images