Cotton Science

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Research on posture and grade recognition of cotton and its localization using improved YOLOX

Xie Jia, Chen Xuefei, Li Yongguo, Jin Changbing, Liang Jintao, Sun Shuaihao

Cotton Science, 2024, 36(4): 328-339. DOI: 10.11963/cs20240016

Fig. 9 Identification test results for different cotton types (A) and cover conditions (B) Cotton_Side represents the side cotton identified based on the YOLOX-Cotton model; Cotton_Top represents the positive normal cotton; CottonLow_Top represents the positive low-grade cotton.

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Table 1 Image source for the dataset
Table 2 Class distribution of training set and test set images
Fig. 1 Equipment for field experiment
Fig. 2 Technology roadmap
Fig. 3 Recognition module network structure in this study Focus is the slicing operation module for the input image; C3 is the concentrated comprehensive convolution block; CBS is the convolution unit; Concat indicates feature stitching; Upsample is feature upsampling; Detect is the detection module; Dark is DarkNet, and Dark+CA is the CA module added after the Dark module.
Fig. 4 The structure of CA module C denotes the depth of the feature channel; H and W denote the height and width of the feature; r is the indentation ratio; X Avg Pool denotes horizontal global pooling; Y Avg Pool denotes vertical global pooling; Conv2d denotes convolutional 2D; BN denotes batch normalization; Sigmoid is an activation function
Fig. 5 Prediction box and ground truth box of the model $b_{c_{x}}^{g t}$, $b_{c_{y}}^{g t}$ are the coordinates of the center point B^gt of the real box (ground truth box), and $b_{c_{x}}$, $b_{c_{y}}$ are the coordinates of the center point B of the prediction box; h and w are the length and width of the prediction box, and h^gt, w^gt are the length and width of the real box; σ is the straight line distance between the point B^gt and the point B; c_h and c_w are the distance between the center point of the two boxes in the vertical direction and the distance in the horizontal direction; c_h and c_w are the height and width of the smallest outer rectangle of the real box and the prediction box; α is the angle between the center point of the real box and the center point of the prediction box in the horizontal direction.
Fig. 6 The central point of the prediction box Point P is the central point of the prediction box. The red box is the prediction box. Cotton_Top is the predicted category, where it represents positive normal cotton. 1.0 is the confidence level.
Fig. 7 Schematic diagram of the disparity calculation The figure uses two directional axes, Z and X; O_left and O_right are the left and right camera optical centers, respectively; P is the positioning point on the object to be measured; P_left and P_right are the imaging points of the positioning point P on the left and right camera optical sensors, respectively; x_left and x_right are the distances of P_left and P_right from the optical axis of the left and right cameras, respectively; f is the camera focal length; b is the distance between the centers of the two cameras; x is the coordinate of point P on the X-axis; Baseline is the line connecting the optical centers of the left and right cameras.
Fig. 8 Training loss function for different models
Table 3 Results of ablation study
Table 4 The training results of different models
Fig. 10 Field cotton category recognition results based on YOLOX-Cotton Cotton_Side represents the side cotton identified based on YOLOX-Cotton model; Cotton_Top represents the positive normal cotton; CottonLow_Top represents the positive side of low-grade cotton. The number at the end of the cotton grade represents confidence.
Table 5 Field verification results of cotton location depth of YOLOX-Cotton model

模态框（Modal）标题

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