A Region-Based Efficient Network for Accurate Object Detection

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).

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

Traitement du signal ◽

10.18280/ts.380319 ◽

2021 ◽

Vol 38 (3) ◽

pp. 719-730

Author(s):

Yurong Guan ◽

Muhammad Aamir ◽

Zhihua Hu ◽

Zaheer Ahmed Dayo ◽

Ziaur Rahman ◽

...

Keyword(s):

Object Detection ◽

Detection Efficiency ◽

High Quality ◽

Softmax Classifier ◽

Body Of Knowledge ◽

Object Locations ◽

Object Proposal ◽

Object Proposals ◽

Traditional Approaches ◽

Image Object Detection

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.

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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.

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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

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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.

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Survey on Image Object Detection Algorithms Based on Deep Learning

Lecture Notes in Computer Science - Artificial Intelligence and Security ◽

10.1007/978-3-030-78609-0_40 ◽

2021 ◽

pp. 468-480

Author(s):

Wei Fang ◽

Liang Shen ◽

Yupeng Chen

Keyword(s):

Deep Learning ◽

Object Detection ◽

Detection Algorithms ◽

Image Object Detection ◽

Image Object

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GC-YOLOv3: You Only Look Once with Global Context Block

Electronics ◽

10.3390/electronics9081235 ◽

2020 ◽

Vol 9 (8) ◽

pp. 1235

Author(s):

Yang Yang ◽

Hongmin Deng

Keyword(s):

Object Detection ◽

Irrelevant Information ◽

Detection Algorithm ◽

Visual Object ◽

Detection Accuracy ◽

Feature Maps ◽

Average Precision ◽

Global Context ◽

Pascal Voc ◽

Feature Pyramid

In order to make the classification and regression of single-stage detectors more accurate, an object detection algorithm named Global Context You-Only-Look-Once v3 (GC-YOLOv3) is proposed based on the You-Only-Look-Once (YOLO) in this paper. Firstly, a better cascading model with learnable semantic fusion between a feature extraction network and a feature pyramid network is designed to improve detection accuracy using a global context block. Secondly, the information to be retained is screened by combining three different scaling feature maps together. Finally, a global self-attention mechanism is used to highlight the useful information of feature maps while suppressing irrelevant information. Experiments show that our GC-YOLOv3 reaches a maximum of 55.5 object detection mean Average Precision (mAP)@0.5 on Common Objects in Context (COCO) 2017 test-dev and that the mAP is 5.1% higher than that of the YOLOv3 algorithm on Pascal Visual Object Classes (PASCAL VOC) 2007 test set. Therefore, experiments indicate that the proposed GC-YOLOv3 model exhibits optimal performance on the PASCAL VOC and COCO datasets.

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