An Object Detection Framework Based on Deep Features and High-Quality Object Locations

Objection detection has long been a fundamental issue in computer vision. Despite being widely studied, it remains a challenging task in the current body of knowledge. Many researchers are eager to develop a more robust and efficient mechanism for object detection. In the extant literature, promising results are achieved by many novel approaches of object detection and classification. However, there is ample room to further enhance the detection efficiency. Therefore, this paper proposes an image object detection and classification, using a deep neural network (DNN) for based on high-quality object locations. The proposed method firstly derives high-quality class-independent object proposals (locations) through computing multiple hierarchical segments with super pixels. Next, the proposals were ranked by region score, i.e., several contours wholly enclosed in the proposed region. After that, the top-ranking object proposal was adopted for post-classification by the DNN. During the post-classification, the network extracts the eigenvectors from the proposals, and then maps the features with the softmax classifier, thereby determining the class of each object. The proposed method was found superior to traditional approaches through an evaluation on Pascal VOC 2007 Dataset.

Download Full-text

A Region-Based Efficient Network for Accurate Object Detection

Traitement du signal ◽

10.18280/ts.380228 ◽

2021 ◽

Vol 38 (2) ◽

pp. 481-494

Author(s):

Yurong Guan ◽

Muhammad Aamir ◽

Zhihua Hu ◽

Waheed Ahmed Abro ◽

Ziaur Rahman ◽

...

Keyword(s):

Object Detection ◽

Detection Efficiency ◽

Visual Object ◽

Average Precision ◽

High Quality ◽

Image Objects ◽

Object Proposal ◽

Proposed Model ◽

Image Object Detection ◽

Image Object

Object detection in images is an important task in image processing and computer vision. Many approaches are available for object detection. For example, there are numerous algorithms for object positioning and classification in images. However, the current methods perform poorly and lack experimental verification. Thus, it is a fascinating and challenging issue to position and classify image objects. Drawing on the recent advances in image object detection, this paper develops a region-baed efficient network for accurate object detection in images. To improve the overall detection performance, image object detection was treated as a twofold problem, involving object proposal generation and object classification. First, a framework was designed to generate high-quality, class-independent, accurate proposals. Then, these proposals, together with their input images, were imported to our network to learn convolutional features. To boost detection efficiency, the number of proposals was reduced by a network refinement module, leaving only a few eligible candidate proposals. After that, the refined candidate proposals were loaded into the detection module to classify the objects. The proposed model was tested on the test set of the famous PASCAL Visual Object Classes Challenge 2007 (VOC2007). The results clearly demonstrate that our model achieved robust overall detection efficiency over existing approaches using fewer or more proposals, in terms of recall, mean average best overlap (MABO), and mean average precision (mAP).

Download Full-text

Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement

Remote Sensing ◽

10.3390/rs13245065 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5065

Author(s):

Bei Cheng ◽

Zhengzhou Li ◽

Hui Li ◽

Zhiquan Ding ◽

Tianqi Qin

Keyword(s):

Object Detection ◽

Learning Algorithm ◽

State Of The Art ◽

Autonomous Learning ◽

Region Merging ◽

Residual Network ◽

High Quality ◽

Association Coefficient ◽

Image Object Detection ◽

Image Object

Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods.

Download Full-text

Improved Object Proposals with Geometrical Features for Autonomous Driving

Mobile Information Systems ◽

10.1155/2017/3175186 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11

Author(s):

Yiliu Feng ◽

Wanzeng Cai ◽

Xiaolong Liu ◽

Huini Fu ◽

Yafei Liu ◽

...

Keyword(s):

Object Detection ◽

Feature Fusion ◽

State Of The Art ◽

Autonomous Driving ◽

Bayesian Framework ◽

Neural Net ◽

High Quality ◽

Geometrical Features ◽

Object Proposals ◽

General Object

This paper aims at generating high-quality object proposals for object detection in autonomous driving. Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene. We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods. In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework. Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly. Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes.

Download Full-text

Spatial hierarchy perception and hard samples metric learning for high-resolution remote sensing image object detection

Applied Intelligence ◽

10.1007/s10489-021-02335-0 ◽

2021 ◽

Author(s):

Dongjun Zhu ◽

Shixiong Xia ◽

Jiaqi Zhao ◽

Yong Zhou ◽

Qiang Niu ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Object Detection ◽

Metric Learning ◽

Remote Sensing Image ◽

Spatial Hierarchy ◽

Image Object Detection ◽

Image Object ◽

Hard Samples

Download Full-text

Bi-directional skip connection feature pyramid network and Sub-pixel convolution for high-quality object detection

Neurocomputing ◽

10.1016/j.neucom.2021.01.021 ◽

2021 ◽

Author(s):

Shuqi Xiong ◽

Xiaohong Wu ◽

Honggang Chen ◽

Linbo Qing ◽

Tong Chen ◽

...

Keyword(s):

Object Detection ◽

High Quality ◽

Feature Pyramid

Download Full-text

Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection

Sensors ◽

10.3390/s18103415 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3415 ◽

Cited By ~ 2

Author(s):

Jinpeng Zhang ◽

Jinming Zhang ◽

Shan Yu

Keyword(s):

Object Detection ◽

Sampling Method ◽

Ground Truth ◽

Detection Accuracy ◽

Two Stage ◽

Baseline Model ◽

Detection Algorithms ◽

Imbalance Problem ◽

Image Object Detection ◽

Image Object

In the image object detection task, a huge number of candidate boxes are generated to match with a relatively very small amount of ground-truth boxes, and through this method the learning samples can be created. But in fact the vast majority of the candidate boxes do not contain valid object instances and should be recognized and rejected during the training and evaluation of the network. This leads to extra high computation burden and a serious imbalance problem between object and none-object samples, thereby impeding the algorithm’s performance. Here we propose a new heuristic sampling method to generate candidate boxes for two-stage detection algorithms. It is generally applicable to the current two-stage detection algorithms to improve their detection performance. Experiments on COCO dataset showed that, relative to the baseline model, this new method could significantly increase the detection accuracy and efficiency.

Download Full-text

Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

Applied Sciences ◽

10.3390/app10062104 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2104

Author(s):

Michał Tomaszewski ◽

Paweł Michalski ◽

Jakub Osuchowski

Keyword(s):

Neural Network ◽

Neural Networks ◽

Object Detection ◽

Convolutional Neural Network ◽

Deep Neural Networks ◽

Detection Efficiency ◽

Training Data ◽

Training Dataset ◽

Training Set ◽

Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

Download Full-text