Research on gangue target detection algorithm based on MBI-YOLOv8

doi:10.11996/JG.j.2095-302X.2024061301

Abstract

Abstract:

To achieve a balance between detection performance and resource consumption in the gangue sorting domain, an efficient, real-time, lightweight object detection algorithm based on an improved YOLOv8 was proposed, suitable for low-performance detection platforms. This algorithm built on the YOLOv8n architecture and incorporated MobileNetv3 to replace the original backbone network, leveraging its lightweight structure to reduce model parameters and computational load, thereby enhancing detection speed. Additionally, the algorithm integrated the BIFPN module for feature enhancement, which employed multi-scale feature fusion to compensate for the loss of detection accuracy associated with the lightweight network, thus achieving model lightweighting while maintaining detection accuracy. Furthermore, the Inner-CIoU bounding box regression loss function was introduced to balance the training results of images with varying qualities, improving the model’s localization capability and further enhancing detection accuracy and speed. To validate the effectiveness of the proposed algorithm, experiments were conducted to compare it with YOLOv3-tiny, YOLOv5n, YOLOv7, and YOLOv8n on a custom dataset. Experimental results demonstrated that the proposed algorithm exhibited optimal overall detection performance. While maintaining detection accuracy, the model’s parameter count was reduced to 1,188,725, representing a 60.46% decrease compared to YOLOv8n. The computational load was reduced from 8.1 GFLOPs to 2.8 GFLOPs, and the FPS increased from 86.02 Hz to 216.58 Hz. This research indicated that the proposed algorithm is a highly efficient, real-time, lightweight gangue detection method with significant potential and advantages in balancing detection performance and computational resource consumption.

Key words: gangue sorting, object detection, real-time performance, YOLOv8n

CLC Number:

LI Zhenfeng, FU Shichen, XU Le, MENG Bo, ZHANG Xin, QING Jianjun. Research on gangue target detection algorithm based on MBI-YOLOv8[J]. Journal of Graphics, 2024, 45(6): 1301-1312.

Figures/Tables 22

References 28

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种类	数量			总计
种类	单目标图像	双目标图像	多目标图像	总计
煤	200	260	1 844	2 400
煤矸石	96	260	1 844	2 400

种类	数量			总计
种类	单目标图像	双目标图像	多目标图像	总计
煤	200	260	1 844	2 400
煤矸石	96	260	1 844	2 400

实验环境		版本型号
硬件配置	CPU	Intel Core i5-13400F
	内存	16 GB
	GPU	NVIDIA GeForce RTX 4060 Ti
	显存	8 GB
	操作环境	Windows 11
软件配置	Python	3.8.19
	Pytorch	1.13.1
	CUDA	11.7
	CUDNN	11.7

实验环境		版本型号
硬件配置	CPU	Intel Core i5-13400F
	内存	16 GB
	GPU	NVIDIA GeForce RTX 4060 Ti
	显存	8 GB
	操作环境	Windows 11
软件配置	Python	3.8.19
	Pytorch	1.13.1
	CUDA	11.7
	CUDNN	11.7

损失函数	mAP0.5/%	mAP0.5:0.95/%	Postprocess per image/ms	FPS/Hz
CIoU	99.5	95.7	0.9	86.02
DIoU	99.5	96.2	0.5	236.27
GIoU	99.5	96.3	0.6	231.62
EIoU	99.5	96.4	1.8	73.17
SIoU	99.5	96.4	2.7	101.91
WIoU v1	99.5	96.5	4.7	85.86
WIoU v2	99.5	96.4	1.2	133.80
WIoU v3	99.5	96.0	1.9	165.41
Inner-CIoU (ratio=0.7)	99.5	96.5	0.7	141.47
Inner-CIoU (ratio=0.75)	99.5	96.3	0.8	128.15
Inner-CIoU (ratio=0.8)	99.5	96.4	0.7	138.73
Inner-DIoU	99.5	96.7	1.0	169.88