scholarly journals Water surface object detection using panoramic vision based on improved single-shot multibox detector

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Aofeng Li ◽  
Xufang Zhu ◽  
Shuo He ◽  
Jiawei Xia
Keyword(s):  
Object Detection ◽  
Water Surface ◽  
Detection Algorithm ◽  
Single Shot ◽  
Panoramic Vision ◽  
Feature Pyramid ◽  
The Mean ◽  
Detection Effect ◽  
Surface Object ◽  
Improved Algorithm

AbstractIn view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection precision of small objects, this study proposes a water surface object detection algorithm from panoramic vision based on an improved SSD. We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. More abundant semantic information of the shallow feature graph is obtained through a feature pyramid network structure with deconvolution. An experiment is conducted by building a water surface object dataset. Results showed the mean Average Precision (mAP) of the improved algorithm are increased by 4.03%, compared with the existing SSD detecting Algorithm. Improved algorithm can effectively improve the overall detection precision of water surface objects and enhance the detection effect of remote objects.

scholarly journals Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Haotian Li ◽  
Kezheng Lin ◽  
Jingxuan Bai ◽  
Ao Li ◽  
Jiali Yu
Keyword(s):  
Object Detection ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Single Shot ◽  
Small Object ◽  
Feature Maps ◽  
Scale Invariant ◽  
Feature Pyramid ◽  
Small Object Detection ◽  
Detection And Localization

In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection, a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed. Firstly, the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure; at the same time, the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel. Then, the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps, and the scale-invariant convolution is performed again on these layers. Finally, the obtained layer is used for detection and localization. The final location coordinates and confidence are output after nonmaximum suppression. Experimental results on the Pascal VOC 2007 and 2012 datasets confirm that there is a 8.2% improvement in mAP compared to the original SSD and some existing algorithms.

scholarly journals Multiobject Detection Algorithm Based on Adaptive Default Box Mechanism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinling Li ◽  
Qingshan Hou ◽  
Jinsheng Xing
Keyword(s):  
Semantic Information ◽  
Detection Algorithm ◽  
Single Shot ◽  
Small Object ◽  
Important Research ◽  
Important Research Topic ◽  
Complex Scenes ◽  
Small Object Detection ◽  
Detection Effect ◽  
Improved Algorithm

Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.

scholarly journals CP-SSD: Context Information Scene Perception Object Detection Based on SSD

2019 ◽  
Vol 9 (14) ◽  
pp. 2785 ◽  
Author(s):  
Yun Jiang ◽  
Tingting Peng ◽  
Ning Tan
Keyword(s):  
Object Detection ◽  
Semantic Information ◽  
Detection Method ◽  
Scene Perception ◽  
Context Information ◽  
Single Shot ◽  
Deep Layers ◽  
Feature Information ◽  
The Mean ◽  
Detection Effect

Single Shot MultiBox Detector (SSD) has achieved good results in object detection but there are problems such as insufficient understanding of context information and loss of features in deep layers. In order to alleviate these problems, we propose a single-shot object detection network Context Perception-SSD (CP-SSD). CP-SSD promotes the network’s understanding of context information by using context information scene perception modules, so as to capture context information for objects of different scales. Deep layer feature map used semantic activation module, through self-supervised learning to adjust the context feature information and channel interdependence, and enhance useful semantic information. CP-SSD was validated on benchmark dataset PASCAL VOC 2007. The experimental results show that, compared with SSD, the mean Average Precision (mAP) of the CP-SSD detection method reaches 77.8%, which is 0.6% higher than that of SSD, and the detection effect was significantly improved in images with difficult to distinguish the object from the background.

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

Electronics ◽  
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.

scholarly journals Object Detection Algorithm Based on Multiheaded Attention

2019 ◽  
Vol 9 (9) ◽  
pp. 1829 ◽  
Author(s):  
Jie Jiang ◽  
Hui Xu ◽  
Shichang Zhang ◽  
Yujie Fang
Keyword(s):  
Object Detection ◽  
Linear Interpolation ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Visual Object ◽  
Single Shot ◽  
Object Class ◽  
Feature Information ◽  
Base Network ◽  
Detector Model

This study proposes a multiheaded object detection algorithm referred to as MANet. The main purpose of the study is to integrate feature layers of different scales based on the attention mechanism and to enhance contextual connections. To achieve this, we first replaced the feed-forward base network of the single-shot detector with the ResNet–101 (inspired by the Deconvolutional Single-Shot Detector) and then applied linear interpolation and the attention mechanism. The information of the feature layers at different scales was fused to improve the accuracy of target detection. The primary contributions of this study are the propositions of (a) a fusion attention mechanism, and (b) a multiheaded attention fusion method. Our final MANet detector model effectively unifies the feature information among the feature layers at different scales, thus enabling it to detect objects with different sizes and with higher precision. We used the 512 × 512 input MANet (the backbone is ResNet–101) to obtain a mean accuracy of 82.7% based on the PASCAL visual object class 2007 test. These results demonstrated that our proposed method yielded better accuracy than those provided by the conventional Single-shot detector (SSD) and other advanced detectors.

scholarly journals Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5315 ◽  
Author(s):  
Fenglong Ding ◽  
Zilong Zhuang ◽  
Ying Liu ◽  
Dong Jiang ◽  
Xiaoan Yan ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Solid Wood ◽  
Single Shot ◽  
Labor Costs ◽  
Color Charge ◽  
Aesthetic Value ◽  
Charge Coupled Device ◽  
Different Types ◽  
The Mean ◽  
Low Efficiency

Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%.

scholarly journals Real-Time Water Surface Object Detection Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3523 ◽  
Author(s):  
Lili Zhang ◽  
Yi Zhang ◽  
Zhen Zhang ◽  
Jie Shen ◽  
Huibin Wang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Water Surface ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
The North ◽  
Aspect Ratios ◽  
High Level ◽  
Surface Object

In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.

scholarly journals AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector

2021 ◽  
Vol 13 (2) ◽  
pp. 160
Author(s):  
Jiangqiao Yan ◽  
Liangjin Zhao ◽  
Wenhui Diao ◽  
Hongqi Wang ◽  
Xian Sun
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Natural Image ◽  
Detection Model ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Scale Detection

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.

scholarly journals Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

2021 ◽  
Vol 13 (22) ◽  
pp. 4610
Author(s):  
Li Zhu ◽  
Zihao Xie ◽  
Jing Luo ◽  
Yuhang Qi ◽  
Liman Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Dynamic Object ◽  
Shared Feature ◽  
Detection Model ◽  
Detection Algorithms ◽  
Adaptive Inference ◽  
Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

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