A Lightweight and Multisource Information Fusion Method for Real-Time Monitoring of Lump Coal on Mining Conveyor Belts

Algorithm 1

Training and valid process of GEB YOLOv5.

	GEB YOLOv5 Algorithm:
	Parameter: Epochs, Batch size, Learning rate, Weight decay coefficient.
	Data Preprocessing: Video single frame, AHE.
	In Put: Training and Valid dataset, Label set.
	Loading: Train models, Valid models.
	Ensure: Algorithm environment, In Put, Backbone, Neck, Out Put.
	training. -th iteration training ():
	Train Net:
	a: Feature Extraction:
	Four groups: Rectangular Conv,
	Ghost Conv, Ghost Bottleneck, ECA.
	b: Feature fusion:
	Ghost Conv, Up sample, Bi FPN_2 and Bi FPN_3.
	c: Positioning error, confidence error, category error, and total loss
	Valid Net:
	a: Test effect of model .
	b: Calculate , and , .
	c: Adjust learning rate and update training strategy.
	Save results of the -th train: weight , and model .
	Update: , .
	Temporary storage model
	Prediction: Identification, Location, Classification.
	Plot: Result curve, Save the best model , Output.
	End Train