Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

Abstract

Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods deal with occlusion yet suffer from the problems of massive manual annotation, separate parameter learning, and low training and detection efficiency. To tackle this, a unified PCM framework (UniPCM) is proposed in this paper. The proposed UniPCM adopts a part sharing mechanism which directly shares the root and part filters of a deformable part-based model (DPM) among different partial configurations. It largely reduces the convolution overhead during both training and detection. In UniPCM, a novel DPM deformation deviation method is proposed for spatial interrelationship estimation of PCM, and a unified weights learning method is presented to simultaneously obtain the weights of elements within each partial configuration and the weights between partial configurations. Experiments on three HR-RSI datasets show that the proposed UniPCM method achieves a much higher training and detection efficiency for POOD compared with state-of-the-art PCM-based methods, while maintaining a comparable detection accuracy. UniPCM obtains a training speedup of maximal 10× and 2.5× for airplane and ship, and a detection speedup of maximal 7.2×, 4.1× and 2.5× on three test sets, respectively.

Occlusion Scene Classification via Cascade Supervised Contrastive Learning

“…Houshman et al [31] and Aly et al [32] used transfer learning to improve the model generalization to occlusion patterns. Qiu et al [29] and Li et al [30] employed semi / unsupervised learning to alleviate the dependency of occlusion annotations. Existing occlusioninvariant feature extraction methods improve the performance significantly, but they view occlusion handling from an isolated perspective.…”

Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

“…In order to measure the performance of the proposed NGBFSEII-BCP method, the existing Traffic Sign Occlusion Detection (TSOD) method [8] and Fully Convolutional Network (FCN) [26] is implemented in MATLAB 2019A simulator using Highway Traffic Dataset [14]. This dataset contains a set of highway traffic videos collected remotely.…”

A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

“…For the last twenty to thirty years, the spatial resolution has been improved significantly. For example, the well-known Quick Bird image has a spatial resolution of 0.6 m, and the newly launched WorldView-3 image has a spatial resolution of 0.3 m. The largely increased spatial resolution makes some applications like object detection, geo-database updating and illegal building detection become possible [14][15][16][17][18][19][20][21]. Usually, remotely sensed imagery with a spatial resolution of about 4 to 1 m is called high-resolution remotely sensed imagery (HRRSI) and remotely sensed imagery with a spatial resolution of less than 1 meter is called very high-resolution remotely sensed imagery (VHRRSI) [22][23][24][25].…”