Structure of the YOLOv7-tiny network.

<p>The backbone network comprises layers of CBS convolution, ELAN-A, and MPConv convolution. Although the ELAN-A layer enhances the network’s feature extraction speed and simplifies the backbone structure of YOLOv7, the parameter and computational overhead remain considerable, making it unable...

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Bibliographic Details
Main Author:	Zhongjian Xie (4633099) (author)
Other Authors:	Xinwei Chen (255125) (author), Weilin Wu (13736) (author), Yao Xiao (227567) (author), Yuanhang Li (5017802) (author), Yaya Zhang (11055897) (author), ZhuXuan Wan (20435933) (author), Weiqi Chen (1806721) (author)
Published:	2025
Subjects:	Biotechnology Cancer Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified range aggregation network mean average precision knowledge distillation techniques high computational intensity experimental results showed experiment significantly enhanced complex mountain environments channel pruning techniques 9 %, 63 8 %, 88 scale feature layer 6 %, 68 6 %, 3 div >< p 8 %, 0 yolov8 ), kpd trichosanthes kirilowii </ gd reached 93 4 %, respectively 0 %, 0 improved network kpd tiny </ p lightweight convolutional module 6 %, kirilowii </ 4 %, feature extraction named kpd study proposes standard convolutions stage algorithms similar studies plant occlusion mainstream single induced blurring improved yolov7 harvesting operations environmental characteristics efficient long dynamic head detection algorithm detection accuracy brightness variation
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Structure of the YOLOv7-tiny network.

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