Citation: Yuan, S.; Du, Y.; Liu, M.;
Yue, S.; Li, B.; Zhang, H.
YOLOv5-Ytiny: A Miniature
Aggregate Detection and
Classification Model. Electronics 2022,
11, 1743. https://doi.org/10.3390/
electronics11111743
Academic Editor: Pedro
Latorre-Carmona
Received: 27 April 2022
Accepted: 26 May 2022
Published: 30 May 2022
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Essay
YOLOv5-Ytiny: A Miniature Aggregate Detection and
Classification Model
Sheng Yuan, Yuying Du *, Mingtang Liu, Shuang Yue, Bin Li and Hao Zhang
College of Information Engineering, North China University of Water Resources and Electric Power,
Zhengzhou 450000, China; yuansheng@ncwu.edu.cn (S.Y.); liumingtang@ncwu.edu.cn (M.L.);
syncwu@yeah.net (S.Y.); mlking44@yeah.net (B.L.); 201616710@stu.ncwu.edu.cn (H.Z.)
* Correspondence: dyyncwu@yeah.net
Abstract:
Aggregate classification is the prerequisite for making concrete. Traditional aggregate
identification methods have the disadvantages of low accuracy and a slow speed. To solve these
problems, a miniature aggregate detection and classification model, based on the improved You
Only Look Once (YOLO) algorithm, named YOLOv5-ytiny is proposed in this study. Firstly, the C3
structure in YOLOv5 is replaced with our proposed CI structure. Then, the redundant part of the
Neck structure is pruned by us. Finally, the bounding box regression loss function GIoU is changed
to the CIoU function. The proposed YOLOv5-ytiny model was compared with other object detection
algorithms such as YOLOv4, YOLOv4-tiny, and SSD. The experimental results demonstrate that
the YOLOv5-ytiny model reaches 9.17 FPS, 60% higher than the original YOLOv5 algorithm, and
reaches 99.6% mAP (the mean average precision). Moreover, the YOLOv5-ytiny model has significant
speed advantages over CPU-only computer devices. This method can not only accurately identify
the aggregate but can also obtain the relative position of the aggregate, which can be effectively used
for aggregate detection.
Keywords: object detection; aggregate; YOLO; classification; computer vision
1. Introduction
Aggregate classification is an important factor for determining the performance and
quality of concrete. Concrete is composed of cement, sand, stones, and water. Aggregate
generally accounts for 70% to 80% of concrete [
1
]. Many factors affect the strength of
concrete, mainly including the cement strength and water–binder ratio, the aggregate
gradation and particle shape, the curing temperature and humidity, the curing age, etc. [
2
].
The aggregate processing system is one of the most important auxiliary production systems
used in the construction of large-scale water conservancy and hydropower projects [
3
].
Aggregate quality control is of great significance to promote the sound development of
the engineering construction industry [
4
], it is also extremely important for improving the
quality of a project and optimizing the cost of a project [
5
]. Different types of aggregate have
different effects on the performance of concrete [
6
]. Regarding the particle size and shape
of the aggregate, the current specifications for coarse aggregate needle-like particles are
relatively broad [
7
], and good-quality aggregate needs to have a standardized particle size
and shape [
8
]. Therefore, we must ensure the quality requirements of aggregate and select
raw materials are reasonable to ensure the quality of concrete. It is particularly important
to find a suitable aggregate classification and detection method.
In recent years, the level of aggregate classification and detection has greatly im-
proved [
9
], and there are now a variety of sand particle size measurement methods. These
include, for example, mesoscale modeling of concrete static and dynamic tensile fractures
for real shape aggregates [
10
], the development of a particle size and shape measurement
system for manufactured sand [
11
], the use of extreme gradient boosting-based pavement
aggregate shape classification [
12
], the use of the wire mesh method to sort aggregate
Electronics 2022, 11, 1743. https://doi.org/10.3390/electronics11111743 https://www.mdpi.com/journal/electronics
