Research on Cotton Terminal Bud Recognition Based on Improved Tiny-YOLOv4 Algorithm
In order to solve the problem of both improving the efficiency of cotton vertex recognition and simplifying the complex model for deployment on edge computing devices,this paper proposes an improved approach based on Tiny-YOLOv4.The backbone network of Tiny-YOLOv4 was structurally optimized,and the numbers of parameters and computational complexity of the model were effectively reduced by re-placing the original first BottleneckCSP module with a more efficient Bottleneck module,simplifying the connections within the module,and passing part of the input feature maps directly to the output.In addi-tion,a miniature CSP-Spatial Pyramid Pooling module was introduced for the detection of cotton terminal buds,which intended to improve the detection accuracy of tiny objects and further reduce the computational burden.It was verified that the improved model reduced the computational complexity of floating-point operations by 33%and the processing time to 0.071 seconds compared to the original Tiny-YOLOv4 mod-el.Results showed that the method could significantly reduce the computational complexity while ensuring efficient recognition,demonstrating its advantages in lightweight deployment.
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