The BiSC aims to enhance semantic information between different feature layers in the backbone network and simultaneously uses the SDCM to improve the receptive fields of differently sized targets in the fusion stage. Finally, SP learns the relationship between the features of upsampling and down...
We employed a bi-directional feature pyramid network (BiFPN) that is used for convolutional multi-scaled feature extraction on the EfficientDet detection architecture, which is novel to the task of FAS. We further use these convolutional multi-scaled features in order to perform deep pixel-wise ...
Then these features are fed into the feature pyramid network and Mix-MLP layer to yield powerful representations without changing sizes. Subse- quently, these features are compressed in parallel, keeping the horizontal dimension unchanged and compressing the vertical one, and keeping th...
It concatenates all camera inputs as a whole into the network, transforms them into the BEV space, and then outputs the result di- rectly. The middle-interaction pipeline comprises a well- defined workflow for feature extraction, space transforma- tion, a...
This study introduces a novel blueberry ripeness and count detection methodology that integrates an attention mechanism with a bi-directional feature pyramid network (BiFPN) within the YOLOv5 framework. The proposed attention mechanism is designed to enhance the YOLOv5 model's focus on pertinent ...
Then, to improve the adaptability for multi-scale defects and reduce the model size, the Bi-directional Feature Pyramid Network (BiFPN) is employed in the neck of YOLOv5 to enhance the feature fusion, where the multi-scale objects can be fully captured. Finally, the decoupled head is ...
In addition, the neck network replaces the basic PANet structure with the bi-directional feature pyramid network module, which introduces multi-scale feature fusion. The experimental results show that the improved YOLOv5 algorithm has an average defect detection accuracy of 97.7%...
YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid NetworkSWINE farmsSWINEK-means clusteringDATA augmentationFEATURE extractionHUMAN resources departmentsIndividual identification of pigs is a critical component of ...
We propose an anchor-free object detector that combines a weighted bi-directional Feature Pyramid Network (BiFPN) and Soft Anchor Point Detector to address the object detection problem in a pixel-wise paradigm. The current mainstream object detection methods are anchor-based, which require to set ...
This paper proposes an enhanced version of You Only Look Once version 5 (YOLOv5) to improve the detection accuracy, where bi-directional feature pyramid network (BiFPN), attention mechanism, and transfer learning are fully integrated. The BiFPN is taken to replace the original feature pyramid ...