Associate professor
Supervisor of Master's Candidates
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Affiliation of Author(s):计算机科学与工程学院
Journal:Frontiers in Plant Science
Funded by:自选课题
Key Words:object detection；forestry pest detection；DETR；Skip connection；small object detection
Abstract:Object detection has a wide range of applications in forestry pest control. However, forest pest detection faces the challenges of a lack of datasets and low accuracy of small target detection. DETR is an end-to-end object detection model based on the transformer, which has the advantages of simple structure and easy migration. However, the object query initialization of DETR is random, and random initialization will cause the model convergence to be slow and unstable. At the same time, the correlation between different network layers is not strong, resulting in DETR is not very ideal in small object training, optimization, and performance. In order to alleviate these problems, we propose Skip DETR, which improves the feature fusion between different network layers through skip connection and the introduction of spatial pyramid pooling layers so as to improve the detection results of small objects. We performed experiments on Forestry Pest Datasets, and the experimental results showed significant AP improvements in our method. When the value of IoU is 0.5, our method is 7.7% higher than the baseline and 6.1% higher than the detection result of small objects. Experimental results show that the application of skip connection and spatial pyramid pooling layer in the detection framework can effectively improve the effect of small-sample obiect detection.
Co-author:贾一新,刘陆洋,dangyuanyuan,songshinan
First Author:Kevin Liu
Indexed by:Journal paper
Volume:14
Page Number:2
ISSN No.:1664-462X
Translation or Not:no
Date of Publication:2023-09-10
