Detection of Male and Female Litchi Flowers Using YOLO-HPFD Multi-Teacher Feature Distillation and FPGA-Embedded Platform

• Published in: Agronomy (Basel) Vol. 13; no. 4; p. 987
• Main Authors: Lyu, Shilei, Zhao, Yawen, Liu, Xueya, Li, Zhen, Wang, Chao, Shen, Jiyuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Design; Detectors; Digital integrated circuits; Distillation; Embedded systems; Females; Field programmable gate arrays; florescence information monitoring; Flowers; FPGA; Fruits; Learning; Litchi; litchi flowers; Males; Methods; Object recognition; Orchards; Platforms; Power consumption; Power management; Semantics; sep feature distillation; Software; Students; Teachers; YOLO; China
• DOI: 10.3390/agronomy13040987
• ISSN: 2073-4395

Litchi florescence has large flower spikes and volume; reasonable control of the ratio of male to female litchi flowers is the key operational aspect of litchi orchards for preserving quality and increasing production. To achieve the rapid detection of male and female litchi flowers, reduce manual statistical errors, and meet the demand for accurate fertilizer regulation, an intelligent detection method for male and female litchi flowers suitable for deployment to low-power embedded platforms is proposed. The method uses multi-teacher pre-activation feature distillation (MPFD) and chooses the relatively complex YOLOv4 and YOLOv5-l as the teacher models and the relatively simple YOLOv4-Tiny as the student model. By dynamically learning the intermediate feature knowledge of the different teacher models, the student model can improve its detection performance by meeting the embedded platform application requirements such as low power consumption and real-time performance. The main objectives of this study are as follows: optimize the distillation position before the activation function (pre-activation) to reduce the feature distillation loss; use the LogCosh-Squared function as the distillation distance loss function to improve distillation performance; adopt the margin-activation method to improve the features of the teacher model passed to the student model; and propose to adopt the Convolution and Group Normalization (Conv-GN) structure for the feature transformation of the student model to prevent effective information loss. Moreover, the distilled student model is quantified and ported for deployment to a field-programmable gate array (FPGA)-embedded platform to design and implement a fast, intelligent detection system for male and female litchi flowers. The experimental results show that compared with an undistilled student model, the mAP of the student model obtained after MPFD feature distillation is improved by 4.42 to 94.21%; the size of the detection model ported and deployed to the FPGA-embedded platform is 5.91 MB, and the power consumption is only 10 W, which is 73.85% and 94.54% lower than that of the detection models on the server and PC platforms, respectively, and it can better meet the application requirements of rapid detection and accurate statistics of male and female litchi flowers.