Hemerocallis citrina Baroni Maturity Detection Method Integrating Lightweight Neural Network and Dual Attention Mechanism

Abstract

North of Shanxi, Datong Yunzhou District is the base for the cultivation of Hemerocallis citrina Baroni, which is the main production and marketing product driving the local economy. Hemerocallis citrina Baroni and other crops’ picking rules are different: the picking cycle is shorter, the frequency is higher, and the picking conditions are harsh. Therefore, in order to reduce the difficulty and workload of picking Hemerocallis citrina Baroni, this paper proposes the GGSC YOLOv5 algorithm, a Hemerocallis citrina Baroni maturity detection method integrating a lightweight neural network and dual attention mechanism, based on a deep learning algorithm. First, Ghost Conv is used to decrease the model complexity and reduce the network layers, number of parameters, and Flops. Subsequently, combining the Ghost Bottleneck micro residual module to reduce the GPU utilization and compress the model size, feature extraction is achieved in a lightweight way. At last, the dual attention mechanism of Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) is introduced to change the tendency of feature extraction and improve detection precision. The experimental results show that the improved GGSC YOLOv5 algorithm reduced the number of parameters and Flops by 63.58% and 68.95%, respectively, and reduced the number of network layers by about 33.12% in terms of model structure. In the case of hardware consumption, GPU utilization is reduced by 44.69%, and the model size was compressed by 63.43%. The detection precision is up to 84.9%, which is an improvement of about 2.55%, and the real-time detection speed increased from 64.16 FPS to 96.96 FPS, an improvement of about 51.13%.