Multi-Scale Fusion Lightweight Target Detection Method for Coal and Gangue Based on EMBS-YOLOv8s
Abstract
:1. Introduction
- (1)
- Aiming at the problems of the low contrast and clarity of coal gangue images caused by low illumination, high conveyor belt speed, and dust interference in the coal separation environment, this paper uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to preprocess the acquired images of coal gangue to improve the quality of the images.
- (2)
- The self-designed Efficient Multi-Branch and Scale Feature Pyramid Network (EMBSFPN) is used in the neck network of the YOLOv8s model, which improves the detection accuracy of the model and also reduces the complexity of the model.
- (3)
- Replacing the CIoU loss function with the Wise-SIoU function at the prediction end of the YOLOv8s model improves the convergence and stability of the model and solves the problem that there is an imbalance of hard and easy samples in the dataset.
2. EMBS-YOLOv8s Model
2.1. Improvements to the Neck Network
2.1.1. Multi-Scale Convolution Block (MSCB)
2.1.2. Efficient Up-Convolution Block (EUCB)
2.2. Improvement of Loss Function
3. Image Acquisition and Preprocessing of Coal Gangue
3.1. Image Acquisition Platform for Coal Gangue
3.2. Image Acquisition of Coal Gangue
3.3. Image Preprocessing for Coal Gangue
- Divide the input image into equal and non-overlapping subblocks (the size is usually in pixels).
- Calculate the histogram H(g) of each subblock separately. (g indicates the gray level of the current subblock area.)
- Set a threshold, T, for the histogram calculated by each sub-block. A histogram’s gray levels that surpass the threshold are clipped, and the portion that does so is then divided equally among all the histogram’s gray levels. The formula for calculating the threshold T is
- 4.
- Perform histogram equalization on the trimmed sub-blocks.
- 5.
- The gray value of pixels is reconstructed by a bilinear interpolation algorithm.
4. Experimental Environment Configuration and Evaluation Indicators
4.1. Experimental Environment Configuration
4.2. Evaluation Indicators
5. Experimental Results and Analysis
5.1. Experimental Results of Different Neck Networks
5.2. Experimental Results of Different Loss Functions
5.3. Ablation Experiment
5.4. Comparative Experiment
6. Conclusions
- (1)
- A coal gangue detection method based on the EMBS-YOLOv8s model is proposed in this paper. By preprocessing the image with CLAHE, the coal gangue image’s clarity and contrast are enhanced. The EMBSFPN structure replaces the original PAN-FPN structure, increasing detection accuracy while decreasing model complexity. The CIoU loss function is improved with the Wise-SIoU loss function; this fixes the imbalance of hard- and easy-to-detect samples in the dataset in addition to enhancing the model’s convergence and stability.
- (2)
- The experimental results show that the average detection accuracy of the EMBS-YOLOv8s model on the self-constructed coal gangue dataset reached 96.0%, which was 2.1% higher than that of the original YOLOv8s model; the number of parameters, computation, and volume of the model were also reduced by 29.59%, 12.68%, and 28.44%, respectively, relative to those of the original YOLOv8s model. Meanwhile, compared with other YOLO series models, the EMBS-YOLOv8s model had high accuracy, low complexity, and better detection speed, and, at the same time, it could also effectively avoid the occurrence of misdetection and omission in complex scenes such as those with low illumination, low noise, and motion blur.
- (3)
- In future work, we will continue to aim at the coexistence of the lightweight characteristic and high accuracy of the model and study a model that can be deployed in the coal gangue sorting robotic system to verify its performance in the actual intelligent sorting of coal gangue. In addition, if we consider the fusion of visible, infrared, or multispectral images to form a multimodal feature for the model to learn in the future, it will effectively improve the accuracy of the detection of coal gangue in different environments.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Evaluation Indicators | Original Image of Coal Gangue | Image of Coal Gangue After CLAHE Processing |
---|---|---|
Laplacian | 1029.69 | 4103.82 |
Mean and Variance | 1.48—brightness is too dark | 0.84—brightness normal |
Name | Configuration |
---|---|
Operating system | Windows11 |
Python | 3.8.2 |
Pytorch | 2.0.1 |
CUDA | 11.8 |
CuDNN | 11.8 |
Neck Network | mAP/% | F1/% | Params/M |
---|---|---|---|
Original structure | 93.9 | 87.15 | 11,126,358 |
MAFPN | 94.6 | 88.69 | 11,190,838 |
BiFPN | 94.9 | 89.71 | 7,365,090 |
EMBSFPN | 95.8 | 90.19 | 7,834,599 |
IOU Loss Functions | mAP/% | F1/% |
---|---|---|
CIoU | 95.8 | 90.19 |
EIoU | 94.3 | 88.05 |
SIoU | 95.0 | 88.62 |
Wise-IoU | 95.1 | 89.35 |
Wise-SIoU | 96.0 | 90.25 |
Group | 1 | 2 | 3 | 4 |
---|---|---|---|---|
CLAHE | √ | √ | √ | |
EMBSFPN | √ | √ | ||
Wise-SIOU | √ | |||
mAP/% | 93.9 | 94.9 | 95.8 | 96.0 |
F1/% | 87.15 | 88.85 | 90.19 | 90.25 |
Params/M | 11,126,358 | 11,126,358 | 7,834,599 | 7,834,599 |
FLOPs/G | 28.4 | 28.4 | 24.8 | 24.8 |
Size/MB | 22.5 | 22.5 | 16.1 | 16.1 |
FPS/f.s−1 | 95.23 | 96.56 | 91.96 | 93.28 |
Model | mAP/% | F1/% | Params/M | FLOPs/G | Model Size/MB | FPS/f.s−1 |
---|---|---|---|---|---|---|
Faster-RCNN-Resnet50 | 92.9 | 85.55 | 41,755,286 | 134.38 | 108.10 | 23.21 |
SSD-Vgg | 90.6 | 79.38 | 35,641,826 | 34.86 | 91.09 | 80.17 |
YOLOv5s | 93.0 | 86.32 | 7,015,519 | 15.8 | 14.5 | 88.91 |
YOLOv7-tiny | 88.5 | 82.06 | 6,010,302 | 13.0 | 12.3 | 102.15 |
YOLOv8n | 93.5 | 86.84 | 3,006,038 | 8.4 | 6.3 | 98.34 |
YOLOv8s | 93.9 | 87.15 | 11,126,358 | 28.4 | 22.5 | 95.23 |
YOLOv10s | 92.7 | 86.50 | 8,036,508 | 24.4 | 16.6 | 91.45 |
YOLOv11s | 91.0 | 85.13 | 9,413,574 | 21.3 | 19.2 | 86.70 |
EMBS-YOLOv8s | 96.0 | 90.25 | 7,834,599 | 24.8 | 16.1 | 93.28 |
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Gao, L.; Yu, P.; Dong, H.; Wang, W. Multi-Scale Fusion Lightweight Target Detection Method for Coal and Gangue Based on EMBS-YOLOv8s. Sensors 2025, 25, 1734. https://doi.org/10.3390/s25061734
Gao L, Yu P, Dong H, Wang W. Multi-Scale Fusion Lightweight Target Detection Method for Coal and Gangue Based on EMBS-YOLOv8s. Sensors. 2025; 25(6):1734. https://doi.org/10.3390/s25061734
Chicago/Turabian StyleGao, Lin, Pengwei Yu, Hongjuan Dong, and Wenjie Wang. 2025. "Multi-Scale Fusion Lightweight Target Detection Method for Coal and Gangue Based on EMBS-YOLOv8s" Sensors 25, no. 6: 1734. https://doi.org/10.3390/s25061734
APA StyleGao, L., Yu, P., Dong, H., & Wang, W. (2025). Multi-Scale Fusion Lightweight Target Detection Method for Coal and Gangue Based on EMBS-YOLOv8s. Sensors, 25(6), 1734. https://doi.org/10.3390/s25061734