基于改进YOLOX模型的芝麻蒴果检测方法研究

Detection Method of Sesame Capsules Based on Improved YOLOX Model

为实现密集条件下芝麻蒴果的准确检测,提出基于YOLOX模型的芝麻蒴果检测定位方法(CE-YOLOX模型)。该模型以CSPDarknet-53作为主干特征提取网络,在路径聚合网络PANet中增加104×104大尺度特征层,增强对目标细粒度特征信息的获取;通过引入注意力机制模块获取目标重要的轮廓特征和空间位置信息;将传统的NMS替换为更有利于重叠目标检测的Soft-NMS算法来降低漏检情况。结果表明,在IoU阈值为0.5时,CE-YOLOX模型在全部测试集上的调和均值(F_(1))、召回率、平均精度分别为0.99、98.65%、99.71%,与原模型YOLOX相比,该模型分别提升了0.05、6.27个百分点、3.28个百分点。通过蒴果计数试验,CE-YOLOX模型计数准确率为96.84%,比YOLOX模型提高了5.28个百分点。改进后的模型CE-YOLOX适用于密集条件下芝麻蒴果检测。

In order to achieve accurate detection of sesame capsules under dense conditions,this study proposes a sesame capsule detection and localization method based on the YOLOX model(CE-YOLOX model). In this model,CSPDarknet-53 is used as the backbone feature extraction network,and a 104×104large-scale feature layer is added to the path aggregation network PANet to strengthen the acquisition of the target fine-grained feature information. By introducing the convolutional block attention module,the important contour features and spatial location information of the object are obtained. The classical NMS is replaced by the Soft-NMS algorithm,which is more conducive to overlapping target detection,to decrease the missed detection. The results showed that the F_(1)average of CE-YOLOX tested on all datasets at IoU threshold of 0. 5 was 0. 99,0. 05 higher than that of YOLOX. The recall rate and average accuracy of CE-YOLOX were 98. 65% and 99. 71%,6. 27 and 3. 28 percentage points higher than that of YOLOX. The counting accuracy of CE-YOLOX was 96. 84%,5. 28 percentage points higher than YOLOX. Consequently,the improved model can recognize sesame capsules under dense conditions.