# An Intelligent Weighted Object Detector for Feature Extraction to Enrich Global Image Information
## 4 Our Proposed Intelligent Weighted Object Detector
### Internal Structure of the Model
## 6 Conclusions and Future Work

In this paper, we take YOLOX-S as the baseline model, add a local image feature extraction operation to chunk the feature map to increase the detailed information of features, and introduce a global feature extraction module to calculate the correlation of feature points to enrich the semantic information of the feature map. To make the model focus on the feature layers that contribute more to the final detection result, we add weights to the feature layers involved in prediction. In addition, this paper also introduces an inexpensive convolution operation to reduce the convolution parameters in the backbone network and proposes the idea that feature maps can be reused. Experimental results show the effectiveness of the above improvements, which achieve improvement of the detection accuracy of the model without increasing the model weights. Finally, according to analysis of the experimental data and concrete reflections on engineering applications, in the future work, we will focus on improving the detection accuracy of complex categories and design detection heads specifically for complex tasks, so that the model can adapt to scenes with overlapping and complex objects. Conceptualization, L.Y. and K.L. ; methodology, L.Y. and K.L. ; software, L.Y., K.L., R.G., C.W. and N.X. ; validation, L.Y., K.L., R.G., C.W. and N.X. ; formal analysis, L.Y., K.L., R.G., C.W. and N.X. ; investigation, L.Y., K.L., R.G., C.W. and N.X. ; resources, L.Y., K.L., R.G., C.W. and N.X. ; data curation, L.Y., K.L., R



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