A model for recognizing farming behaviors of plantation workers

Xu, Jinpo; Zhao, W. R.; Wei, Chunyan; Hu, Xiaonan; Li, Xiang

doi:10.1016/j.compag.2022.107395

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the domain of agricultural behavior recognition, Xu Jinbo et al introduced an enhanced ConvLSTM model [24] and an improved (2 + 1)D model [25] for the recognition of four types of agricultural behaviors in 2021 and 2022. Additionally, Yang Xinting et al devised a methodology for recognizing two types of agricultural behaviors using the YOLOv7-tiny model in 2023 [26].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture

Zou,

Zhou,

Jiang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Field manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people’s behavior during field labor. In this study, we propose a field manual labor behavior recognition network based on an enhanced SlowFast architecture. The main work includes the following aspects: first, we constructed a field manual labor behavior dataset containing 433,500 fast-track frames and 8670 key frames based on the captured video data, and labeled it in detail; this includes 9832 labeled frames. This dataset provides a solid foundation for subsequent studies. Second, we improved the slow branch of the SlowFast network by introducing the combined CA (Channel Attention) attention module. Third, we enhanced the fast branch of the SlowFast network by introducing the ACTION hybrid attention module. The experimental results show that the recognition accuracy of the improved SlowFast network model with the integration of the two attention modules increases by 7.08%. This implies that the improved network model can more accurately locate and identify manual labor behavior in the field, providing a more effective method for problem solving.

show abstract

Section: Introductionmentioning

confidence: 99%