## Appendix A
### Prediction  
In order to classify and regression localize the effective feature layers of three different scales, the original coupled head is discarded in the precision part of ECA-YOLOX-Tiny, meanwhile, the decoupled head, anchor-free mechanism and simplified optimal transport assignment (SimOTA) strategy are introduced innovatively.  
1. Decoupled Head  
In the past YOLO series, the classification and regression are realized by a \(1\times 1\) convolution layer in the detection head (coupled head). However, there is a difference between classification and regression, the classification problem mainly considers the difference between each target object, and the regression problem mainly considers the characteristics of object contour boundary. Compared with coupled head (shown in Figure 15), the \(1\times 1\) convolution layer is used to reduce the channel dimension in decoupled head, and two parallel branches are added behind the \(1\times 1\) convolution layer for classification and regression, respectively, by which the convergence speed and model detection accuracy can be effectively improved at a lower computational cost.  
2. Anchor-free  
The target detection networks based on the anchor-free mechanism are at present considered as a new type and friendly networks for industrial applications. Compared with the anchor-based mechanism, the anchor-free mechanism detects targets through key points without anchor boxes, by which the number of model parameters and the computational complexity can be significantly reduced, meanwhile, the work of model training and detection phases is quite simple and the same performance as the anchor-based mechanism is obtained. Therefore, the anchor-free mechanism is adopted in the ECA-YOLOX-Tiny model to reduce the prediction quantity of each position from three (coordinate offset, confidence, and classification) to one (coordinate offset), and the four parameters of predicted bounding boxes (the offsets of the \(x\) and \(y\) axes, height and width) are directly predicted.
3. SimOTA  
In the convolution neural network model, the intersection over union (IoU) between the truth bounding boxes and the predicted bounding boxes is used to divide the positive and negative samples. Under the different condition of sizes, shapes and occlusions, etc., the assignment of positive and negative samples needs to be considered globally. Generally, the predicted bounding boxes assignment problem is regarded as an optimal transport problem of linear programming. The optimal transport assignment (OTA) solves the predicted bounding boxes allocation problem from the global perspective so as to match the truth bounding boxes with the predicted bounding boxes at the lowest overall cost. Supposed that there are \(K\) truth bounding boxes and \(M\) predicted bounding boxes, the cost matrix size is \(K\times M\), and the elements in the matrix are the loss values of the truth bounding boxes and predicted bounding boxes. The smaller the loss is, the smaller the cost of selecting the truth bounding boxes and predicted bounding boxes. Based on OTA, the Sinkhorn-Knopp algorithm is eliminated in the dynamic label assignment strategy (SimOTA), which not only reduces the training time of the model and makes the distribution of positive and negative samples more balanced, but also avoids the extra hyperparameters problem brought by the Sinkhorn-Knopp algorithm.