.png){kind=icon}
Return
LKC-Net: large kernel convolution object detection network
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-023-36724-x.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Deep learning-based object detection methods have achieved great performance improvement. However, since small kernel convolution has been widely used, the semantic feature is difficult to obtain due to the small receptive fields, and the key information cannot be highlighted, resulting in a series of problems such as wrong detection, missing detection, and repeated detection. To overcome these problems, we propose a large kernel convolution object detection network based on feature capture enhancement and vast receptive field attention, called LKC-Net. Firstly, a feature capture enhancement block based on large kernel convolution is proposed to improve the semantic feature capturing ability, and depth convolution is used to reduce the number of parameters. Then, the vast receptive filed attention mechanism is constructed to enhance channel direction information extraction ability, and it is more compatible with the proposed backbone than other existing attention mechanisms. Finally, the loss function is improved by introducing the SIoU, which can overcome the angle mismatch problem between the ground truth and prediction box. Experiments are conducted on Pascal VOC and MS COCO datasets for demonstrating the performance of LKC-Net.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
27.9W
Citations:
83.5W
Organization
Cited Papers
ShareSaveShareSaveChiral cage molecules generated by regioselective O-alkylation of a doubly-bridged calix[8]arene
PREAI
ShareSaveCandidate .gamma.-Secretases in the Generation of the Carboxyl Terminus of the Alzheimer's Disease .beta.A4 Amyloid: Possible Involvement of Cathepsin D.png)
Biochemistry
BiochemistryIF0
PREAI
ShareSave
ShareSaveShareSaveShareSaveShareSaveShareSave
View more