scholarly journals FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection

2019 ◽  
Vol 9 (20) ◽  
pp. 4276 ◽  
Author(s):  
Wenxu Shi ◽  
Shengli Bao ◽  
Dailun Tan
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Single Shot ◽  
Feature Enhancement ◽  
Input Size ◽  
Pyramid Structure ◽  
Deep Layers ◽  
Extended Experiment ◽  
Conventional Structure ◽  
Better Than

The Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current target detection field. It has achieved good results in target detection but there are problems such as poor extraction of features in shallow layers and loss of features in deep layers. In this paper, we propose an accurate and efficient target detection method, named Single Shot Object Detection with Feature Enhancement and Fusion (FFESSD), which is to enhance and exploit the shallow and deep features in the feature pyramid structure of the SSD algorithm. To achieve it we introduced the Feature Fusion Module and two Feature Enhancement Modules, and integrated them into the conventional structure of the SSD. Experimental results on the PASCAL VOC 2007 dataset demonstrated that FFESSD achieved 79.1% mean average precision (mAP) at the speed of 54.3 frame per second (FPS) with the input size 300 × 300, while FFESSD with a 512 × 512 sized input achieved 81.8% mAP at 30.2 FPS. The proposed network shows state-of-the-art mAP, which is better than the conventional SSD, Deconvolutional Single Shot Detector (DSSD), Feature-Fusion SSD (FSSD), and other advanced detectors. On extended experiment, the performance of FFESSD in fuzzy target detection was better than the conventional SSD.

scholarly journals An Approach to Improve SSD through Skip Connection of Multiscale Feature Maps

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaoguo Zhang ◽  
Ye Gao ◽  
Fei Ye ◽  
Qihan Liu ◽  
Kaixin Zhang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Semantic Information ◽  
Feature Fusion ◽  
Poor Performance ◽  
Detection Performance ◽  
Single Shot ◽  
Small Object ◽  
Feature Maps ◽  
Input Size

SSD (Single Shot MultiBox Detector) is one of the best object detection algorithms and is able to provide high accurate object detection performance in real time. However, SSD shows relatively poor performance on small object detection because its shallow prediction layer, which is responsible for detecting small objects, lacks enough semantic information. To overcome this problem, SKIPSSD, an improved SSD with a novel skip connection of multiscale feature maps, is proposed in this paper to enhance the semantic information and the details of the prediction layers through skippingly fusing high-level and low-level feature maps. For the detail of the fusion methods, we design two feature fusion modules and multiple fusion strategies to improve the SSD detector’s sensitivity and perception ability. Experimental results on the PASCAL VOC2007 test set demonstrate that SKIPSSD significantly improves the detection performance and outperforms lots of state-of-the-art object detectors. With an input size of 300 × 300, SKIPSSD achieves 79.0% mAP (mean average precision) at 38.7 FPS (frame per second) on a single 1080 GPU, 1.8% higher than the mAP of SSD while still keeping the real-time detection speed.

scholarly journals FE-YOLO: A Feature Enhancement Network for Remote Sensing Target Detection

2021 ◽  
Vol 13 (7) ◽  
pp. 1311
Author(s):  
Danqing Xu ◽  
Yiquan Wu
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
State Of The Art ◽  
Computing Power ◽  
Fast Detection ◽  
Feature Enhancement ◽  
The Past ◽  
Small Targets ◽  
Better Than ◽  
Great Development

In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

scholarly journals TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

2021 ◽  
Vol 13 (13) ◽  
pp. 2558
Author(s):  
Lei Yu ◽  
Haoyu Wu ◽  
Zhi Zhong ◽  
Liying Zheng ◽  
Qiuyue Deng ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Methods ◽  
Observation System ◽  
Single Shot ◽  
Small Target ◽  
Feature Mapping ◽  
Sar Images ◽  
Two Stage ◽  
Ship Detection ◽  
Deep Feature

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.

scholarly journals A Real-Time Structure of Attitude Algorithm for High Dynamic Bodies

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Xingcheng Li ◽  
Shuangbiao Zhang
Keyword(s):  
Real Time ◽  
Time Structure ◽  
Flow Diagram ◽  
The Real ◽  
Time Problem ◽  
Conventional Structure ◽  
High Dynamic ◽  
Two Stages ◽  
Better Than ◽  
Attitude Matrix

To solve the real-time problem of attitude algorithm for high dynamic bodies, a real-time structure of attitude algorithm is developed by analyzing the conventional structure that has two stages, and a flow diagram of a real-time structure for a Matlab program is provided in detail. During the update of the attitude matrix, the real-time structure saves every element of attitude matrix in minor loop in real time and updates the next attitude matrix based on the previous matrix every subsample time. Thus, the real-time structure avoids lowering updating frequency, though the multisubsample algorithms are used. Simulation and analysis show that the real-time structure of attitude algorithm is better than the conventional structure due to short update time of attitude matrix and small drifting error, and it is more appropriate for high dynamic bodies.

scholarly journals DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6780
Author(s):  
Zhitong Lai ◽  
Rui Tian ◽  
Zhiguo Wu ◽  
Nannan Ding ◽  
Linjian Sun ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Feature Fusion ◽  
State Of The Art ◽  
Depth Estimation ◽  
Multi Scale ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
The Common ◽  
Monocular Depth ◽  
Multiple Stages

Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.

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