A novel approach to coal pile location and recognition

which is based on entropy energy

is proposed in this paper to solve interference problems in a complex environment

which inhibit detection efficiency in coal mines. First

continuous information sampling units are scattered in the image

and the distribution area and gray levels of the larger probability density in the sampling unit are determined. All energy frequencies of the sampling unit are then calculated

the weakest of which is removed by filtration

and the edge of coal pile is calculated by second-order partial derivatives. The sampling unit is moved to the edge of the coal pile. Finally

the early warning based on the coal pile height and radius is ready for the upper limit

which is calculated according to the sampling unit. The average processing speed of the single image is 0.35 seconds per frame

the maximum of coal accident warning time interval is 0.35 seconds

and the average recognition accuracy is 97.1 percent

which is the method text results on the coal pile videos of coal mines. Experiments show that. This method can overcome the effects of noise and blurred image on the recognition of coal pile by taking advantage of the ability of entropy energy to identify a fuzzy edge and its resistance to environmental noise. Experiments show that the proposed approach has good adaptability to coal pile location and recognition

and is of significant and practical value to improve the efficiency and safety of coal mine production.