Attention-Based Feature Pyramid Network for Object Detection

2019 ◽  
Author(s):  
Ziyuan Liu ◽  
Ping Gong ◽  
Jing Wang
Keyword(s):  
Object Detection ◽  
Feature Pyramid

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

2021 ◽  
Author(s):  
Shuqi Xiong ◽  
Xiaohong Wu ◽  
Honggang Chen ◽  
Linbo Qing ◽  
Tong Chen ◽  
...  
Keyword(s):  
Object Detection ◽  
High Quality ◽  
Feature Pyramid

Parallel Feature Pyramid Network for Object Detection

2018 ◽  
pp. 239-256 ◽  
Author(s):  
Seung-Wook Kim ◽  
Hyong-Keun Kook ◽  
Jee-Young Sun ◽  
Mun-Cheon Kang ◽  
Sung-Jea Ko
Keyword(s):  
Object Detection ◽  
Feature Pyramid ◽  
Parallel Feature

scholarly journals Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

2020 ◽  
Vol 12 (5) ◽  
pp. 784 ◽  
Author(s):  
Wei Guo ◽  
Weihong Li ◽  
Weiguo Gong ◽  
Jinkai Cui
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Semantic Information ◽  
Aerial Images ◽  
Training Strategy ◽  
The Public ◽  
Multi Scale ◽  
Shallow Layer ◽  
The Neural Network ◽  
Feature Pyramid

Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.

Dual-bottleneck feature pyramid network for multiscale object detection

2022 ◽  
Vol 31 (01) ◽  
Author(s):  
Suting Chen ◽  
Wenyan Ma ◽  
Liangchen Zhang
Keyword(s):  
Object Detection ◽  
Feature Pyramid

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.

Scale-aware feature pyramid architecture for marine object detection

2020 ◽  
Author(s):  
Fengqiang Xu ◽  
Huibing Wang ◽  
Jinjia Peng ◽  
Xianping Fu
Keyword(s):  
Object Detection ◽  
Feature Pyramid
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