Qiu Shujin, Gao Jian, Han Mengyao, Cui Qingliang, Yuan Xiangyang, Wu Cuiqing
College of Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Dryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Jinzhong 030801, China.
Sensors (Basel). 2024 Dec 27;25(1):104. doi: 10.3390/s25010104.
In order to solve the problems of high planting density, similar color, and serious occlusion between spikes in sorghum fields, such as difficult identification and detection of sorghum spikes, low accuracy and high false detection, and missed detection rates, this study proposes an improved sorghum spike detection method based on YOLOv8s. The method involves augmenting the information fusion capability of the YOLOv8 model's neck module by integrating the Gold feature pyramid module. Additionally, the SPPF module is refined with the LSKA attention mechanism to heighten focus on critical features. To tackle class imbalance in sorghum detection and expedite model convergence, a loss function incorporating Focal-EIOU is employed. Consequently, the YOLOv8s-Gold-LSKA model, based on the Gold module and LSKA attention mechanism, is developed. Experimental results demonstrate that this improved method significantly enhances sorghum spike detection accuracy in natural field settings. The improved model achieved a precision of 90.72%, recall of 76.81%, mean average precision (mAP) of 85.86%, and an F1-score of 81.19%. Comparing the improved model of this study with the three target detection models of YOLOv5s, SSD, and YOLOv8, respectively, the improved model of this study has better detection performance. This advancement provides technical support for the rapid and accurate recognition of multiple sorghum spike targets in natural field backgrounds, thereby improving sorghum yield estimation accuracy. It also contributes to increased sorghum production and harvest, as well as the enhancement of intelligent harvesting equipment for agricultural machinery.
为了解决高粱田中种植密度高、颜色相近、穗间遮挡严重等问题，如高粱穗识别检测困难、准确率低、误检率高以及漏检率高等问题，本研究提出了一种基于YOLOv8s的改进高粱穗检测方法。该方法通过集成Gold特征金字塔模块来增强YOLOv8模型颈部模块的信息融合能力。此外，利用LSKA注意力机制对SPPF模块进行优化，以增强对关键特征的关注。为了解决高粱检测中的类别不平衡问题并加速模型收敛，采用了结合Focal-EIOU的损失函数。在此基础上，开发了基于Gold模块和LSKA注意力机制的YOLOv8s-Gold-LSKA模型。实验结果表明，这种改进方法显著提高了自然田间环境下高粱穗的检测准确率。改进后的模型精度达到90.72%，召回率为76.81%，平均精度均值（mAP）为85.86%，F1分数为81.19%。将本研究的改进模型分别与YOLOv5s、SSD和YOLOv8这三种目标检测模型进行比较，本研究的改进模型具有更好的检测性能。这一进展为自然田间背景下多个高粱穗目标的快速准确识别提供了技术支持，从而提高了高粱产量估计的准确性。它还有助于提高高粱的生产和收获量，以及增强农业机械的智能收获设备。
