Object detection method based on dense connection and feature fusion

Object detection-based deep learning by using the looking and thinking twice mechanism plays an important role in electrical construction work. Nevertheless, the use of this mechanism in object detection produces some problems, such as calculation pressure caused by multilayer convolution and redundant features that confuse the network. In this paper, we propose a self-recurrent learning and gap sample feature fusion-based object detection method to solve the aforementioned problems. The network consists of three modules: self-recurrent learning-based feature fusion (SLFF), residual enhancement architecture-based multichannel (REAML), and gap sample-based features fusion (GSFF). SLFF detects objects in the background through an iterative convolutional network. REAML, which serves as an information filtering module, is used to reduce the interference of redundant features in the background. GSFF adds feature augmentation to the network. Simultaneously, our model can effectively improve the operation and production efficiency of electric power companies’ personnel and guarantee the safety of lives and properties.

Download Full-text

A keypoint-based object detection method with attention mechanism and feature fusion

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9326802 ◽

2020 ◽

Author(s):

Hui Wang ◽

Tangwen Yang

Keyword(s):

Object Detection ◽

Detection Method ◽

Feature Fusion ◽

Attention Mechanism

Download Full-text

Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images

Remote Sensing ◽

10.3390/rs13183776 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3776

Author(s):

Linlin Zhu ◽

Xun Geng ◽

Zheng Li ◽

Chun Liu

Keyword(s):

Object Detection ◽

Detection Method ◽

Feature Fusion ◽

Attention Mechanism ◽

Detection Methods ◽

Visual Object ◽

Art Object ◽

Geological Processes ◽

Landing Sites ◽

New Feature

It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.

Download Full-text

Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

Computational Intelligence and Neuroscience ◽

10.1155/2021/7618828 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Liming Zhou ◽

Haoxin Yan ◽

Chang Zheng ◽

Xiaohan Rao ◽

Yahui Li ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Detection Method ◽

Feature Fusion ◽

Remote Sensing Image ◽

High Rate ◽

Detection Methods ◽

Detection Accuracy ◽

Aircraft Detection ◽

Low Rate

Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.

Download Full-text