## Appendix A  
Based on the YOLOX-Tiny model, the key techniques of backbone feature extraction network (Backbone), enhanced feature extraction network (Neck), and prediction sections of ECA-YOLOX-Tiny (decoupled head, anchor-free, and simplified dynamic label matching strategy SimOTA) are described in the following. 1. Backbone Feature Extraction Network (Backbone)  
The backbone network is used to preliminarily extract the feature and obtain three initial effective feature layers for multi-scale feature fusion. The backbone network of ECA-YOLOX-Tiny in Figure 3 is CSPDarknet, for which the CSP structure (CSPLayer) of cross-stage partial network (CSPNet) is adopted. A series of small residual units (Res Units) in a stacked structure are adopted in the backbone branch of CSPLayer, and the larger residual edges are built on the side branches. Through the large residual edges, the input and output of the backbone branch are directly connected by the simple channel dimension adjustment method. The main branch of the residual unit is a \(1\times 1\) convolution connected to a \(3\times 3\) convolution, and there is no special treatment for residual edges and the direct addition operation between inputs and outputs of the main branch is designed. The structure of the CSPLayer as well as the Res Unit is shown in Figure A1a,b. The abundant gradient combination can be achieved by using cross-stage splitting and merging strategies in the CSPLayer module, and the excessive and repetitive gradient information can be prevented by the method of gradient flow truncation. The CSPLayer module not only has a small amount of calculation and memory consumption, but also enhances the learning ability of the network. In the CSPLayer module, the residual units are easy to optimize, and the shortcut connections are used to improve the feature extraction ability of the model, by which the problems of gradient disappearance, gradient explosion and model degradation caused by the increase in network depth can be alleviated. Similar to the neighboring down-sampling, a special down-sampling method (focus network structure) is introduced in YOLOV5, for which four independent low-resolution feature maps are firstly reconstructed from a high-resolution image by taking a value every other pixel, and then four independent feature maps are stacked. Additionally, the number of feature map channels increases four times, the length and width of the feature maps are half that of the original feature maps, the width and height information of the feature points are stacked on the channel space, and the receptive field of the feature points is improved. The problem of information loss caused by down-sampling of the original images can be solved by the special down-sampling method, and two-fold down-sampling feature maps without information loss can finally be obtained. The focus network structure is borrowed in ECA-YOLOX-Tiny to reduce model computation and memory occupation as well as improve model speed. The focus network structure and its corresponding feature map transformations are shown in Figure A2a,b. A new SiLU activation function [32] expressed in (A1) is employed in ECA-YOLOX-Tiny. \[SiLU(x)=x\cdot\text{sigmoid}(x)\] (A1)  
where \(x\) is the inputs of model. Compared with ReLU, the smooth output of SiLU is more ergodic, and the sensitivity of model initialization and learning rate is reduced. The non-monotonicity of SiLU increases the model expressiveness and improves the gradient flow, the robustness can be provided in light of the different initialization and learning rate. When the function lies in the saturation region, the training is very slow due to the small gradient, but the loss function saturation condition can be avoided because there is no compression gradient of SiLU. In ECA-YOLOX-Tiny model, the SPPBottleneck module is applied to the end of the backbone network, and the different scales' feature information is mixed together through the parallel maximum pooling layers with different kernel sizes.