Vehicle Detection Based on Superpixel and Improved HOG in Aerial Images

Guo, Enlai; Bai, Lianfa; Zhang, Yi; Han, Jing

doi:10.1007/978-3-319-71607-7_32

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…As the over-segmented image becomes graph-structure data, most of the previous superpixel-wise segmentation methods employ the probabilistic graphical models (e.g., Conditional Random Fields (CRF) [11,12]) to segment the nodes of the graphs [13]. Probabilistic graphical models can capture the appearance and spatial consistency [14,15], but they usually have a heavy computational burden [16]. Take CRF as an example: in addition to learning the coefficients utilizing Structured Support Vector Machine (SSVM) [17] or other classifiers [18][19][20], the testing stage of CRF also requires an inference algorithm to find the optimal label sequence by pursuing the Maximum A Posteriori (MAP).…”

Section: Introductionmentioning

confidence: 99%

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

Gao

Sun

et al. 2019

Remote Sensing

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The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks.

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Section: Introductionmentioning

confidence: 99%

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

Gao

Sun

et al. 2019

Remote Sensing

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“…The traditional methods refer to traditional machine learning algorithms. References [1,2,3] adopted the histogram of oriented gradient (HOG) method to extract vehicle-type features in images, and then classified those features using the support vector machine (SVM), thus achieving vehicle detection. In Reference [4], a deformable part model (DPM) was proposed for vehicle detection and obtained a good result.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv2 for Vehicle Detection

Sang

Guo

et al. 2018

Sensors

169

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Vehicle detection is one of the important applications of object detection in intelligent transportation systems. It aims to extract specific vehicle-type information from pictures or videos containing vehicles. To solve the problems of existing vehicle detection, such as the lack of vehicle-type recognition, low detection accuracy, and slow speed, a new vehicle detection model YOLOv2_Vehicle based on YOLOv2 is proposed in this paper. The k-means++ clustering algorithm was used to cluster the vehicle bounding boxes on the training dataset, and six anchor boxes with different sizes were selected. Considering that the different scales of the vehicles may influence the vehicle detection model, normalization was applied to improve the loss calculation method for length and width of bounding boxes. To improve the feature extraction ability of the network, the multi-layer feature fusion strategy was adopted, and the repeated convolution layers in high layers were removed. The experimental results on the Beijing Institute of Technology (BIT)-Vehicle validation dataset demonstrated that the mean Average Precision (mAP) could reach 94.78%. The proposed model also showed excellent generalization ability on the CompCars test dataset, where the “vehicle face” is quite different from the training dataset. With the comparison experiments, it was proven that the proposed method is effective for vehicle detection. In addition, with network visualization, the proposed model showed excellent feature extraction ability.

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“…However, effective feature extraction algorithms for superpixels remain a challenge. Most of the previous methods focus on hand-crafted features for superpixels, e.g., histogram of oriented gradients (HOG) [21], gray-level co-occurrence matrix (GLCM) [22], and co-occurrence matrix (COOC) [23]. These hand-crafted features include many coefficients determined by previous knowledge and experiences in practice [19], which is difficult and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

Gao

Sun

et al. 2019

Remote Sensing

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Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented into a set of primitive superpixels. This algorithm combines hierarchical conditional generative adversarial nets (CGAN) and conditional random fields (CRF). The CGAN-based networks can leverage abundant unlabeled data learning parameters, reducing their reliance on the labeled samples. In order to preserve neighborhood consistency in the feature extraction stage, the hierarchical CGAN is composed of two sub-networks, which are employed to extract the information of the central superpixels and the corresponding background superpixels, respectively. Afterwards, CRF is utilized to perform label optimization using the concatenated features. Quantified experiments on an airborne SAR image dataset prove that the proposed method can effectively learn feature representations and achieve competitive accuracy to the state-of-the-art segmentation approaches. More specifically, our algorithm has a higher Cohen's kappa coefficient and overall accuracy. Its computation time is less than the current mainstream pixel-level semantic segmentation networks.

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Vehicle Detection Based on Superpixel and Improved HOG in Aerial Images

Cited by 13 publications

References 6 publications

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

An Improved YOLOv2 for Vehicle Detection

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

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