scholarly journals IYOLO: Multi-scale infrared target detection method based on bidirectional feature fusion

2021 ◽  
Vol 1873 (1) ◽  
pp. 012020
Author(s):  
Xiaofeng Zhao ◽  
Yebin Xu ◽  
Fei Wu ◽  
Wei Cai ◽  
Zhili Zhang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Multi Scale

scholarly journals CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection

2021 ◽  
Vol 13 (5) ◽  
pp. 847
Author(s):  
Wei Huang ◽  
Guanyi Li ◽  
Qiqiang Chen ◽  
Ming Ju ◽  
Jiantao Qu
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Target Detection ◽  
Feature Fusion ◽  
Detection Methods ◽  
Natural Image ◽  
Scale Feature ◽  
Multi Scale ◽  
Backbone Network ◽  
Scale Detection

In the wake of developments in remote sensing, the application of target detection of remote sensing is of increasing interest. Unfortunately, unlike natural image processing, remote sensing image processing involves dealing with large variations in object size, which poses a great challenge to researchers. Although traditional multi-scale detection networks have been successful in solving problems with such large variations, they still have certain limitations: (1) The traditional multi-scale detection methods note the scale of features but ignore the correlation between feature levels. Each feature map is represented by a single layer of the backbone network, and the extracted features are not comprehensive enough. For example, the SSD network uses the features extracted from the backbone network at different scales directly for detection, resulting in the loss of a large amount of contextual information. (2) These methods combine with inherent backbone classification networks to perform detection tasks. RetinaNet is just a combination of the ResNet-101 classification network and FPN network to perform the detection tasks; however, there are differences in object classification and detection tasks. To address these issues, a cross-scale feature fusion pyramid network (CF2PN) is proposed. First and foremost, a cross-scale fusion module (CSFM) is introduced to extract sufficiently comprehensive semantic information from features for performing multi-scale fusion. Moreover, a feature pyramid for target detection utilizing thinning U-shaped modules (TUMs) performs the multi-level fusion of the features. Eventually, a focal loss in the prediction section is used to control the large number of negative samples generated during the feature fusion process. The new architecture of the network proposed in this paper is verified by DIOR and RSOD dataset. The experimental results show that the performance of this method is improved by 2–12% in the DIOR dataset and RSOD dataset compared with the current SOTA target detection methods.

scholarly journals The Application of Improved YOLO V3 in Multi-Scale Target Detection

2019 ◽  
Vol 9 (18) ◽  
pp. 3775 ◽  
Author(s):  
Ju ◽  
Luo ◽  
Wang ◽  
Hui ◽  
Chang
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Research Directions ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Derivation Method ◽  
Small Targets ◽  
Mathematical Derivation

Target detection is one of the most important research directions in computer vision. Recently, a variety of target detection algorithms have been proposed. Since the targets have varying sizes in a scene, it is essential to be able to detect the targets at different scales. To improve the detection performance of targets with different sizes, a multi-scale target detection algorithm was proposed involving improved YOLO (You Only Look Once) V3. The main contributions of our work include: (1) a mathematical derivation method based on Intersection over Union (IOU) was proposed to select the number and the aspect ratio dimensions of the candidate anchor boxes for each scale of the improved YOLO V3; (2) To further improve the detection performance of the network, the detection scales of YOLO V3 have been extended from 3 to 4 and the feature fusion target detection layer downsampled by 4× is established to detect the small targets; (3) To avoid gradient fading and enhance the reuse of the features, the six convolutional layers in front of the output detection layer are transformed into two residual units. The experimental results upon PASCAL VOC dataset and KITTI dataset show that the proposed method has obtained better performance than other state-of-the-art target detection algorithms.

scholarly journals Pedestrian and cyclist detection based on deep neural network fast R-CNN

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141982965 ◽  
Author(s):  
Kelong Wang ◽  
Wei Zhou
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Detection Method ◽  
Feature Fusion ◽  
Intelligent Vehicles ◽  
Urban Traffic ◽  
Joint Detection ◽  
Difficult Case ◽  
Important Basis

In this article, a unified joint detection framework for pedestrian and cyclist is established to realize the joint detection of pedestrian and cyclist targets. Based on the target detection of fast regional convolution neural network, a deep neural network model suitable for pedestrian and cyclist detection is established. Experiments for poor detection results for small-sized targets and complex and changeable background environment; various network improvement schemes such as difficult case extraction, multilayer feature fusion, and multitarget candidate region input were designed to improve detection and to solve the problems of frequent false detections and missed detections in pedestrian and cyclist target detection. Results of experimental verification of the pedestrian and cyclist database established in Beijing’s urban traffic environment showed that the proposed joint detection method for pedestrians and cyclists can realize the stable tracking of joint detection and clearly distinguish different target categories. Therefore, an important basis for the behavior decision of intelligent vehicles is provided.

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