# An Intelligent Weighted Object Detector for Feature Extraction to Enrich Global Image Information
###### Abstract  
Object detection is a fundamental task in computer vision. To improve the detection accuracy of a detection model without increasing the model weights, this paper modifies the YOLOX model by first replacing some of the traditional convolution operations in the backbone network to reduce the parameter cost of generating feature maps. We design a local feature extraction module to chunk the feature maps to obtain local image features and a global feature extraction module to calculate the correlation between feature points to enrich the feature, and add learnable weights to the feature layers involved in the final prediction to assist the model in detection. Moreover, the idea of feature map reuse is proposed to retain more information from the high-dimensional feature maps. In comparison experiments on the dataset PASCAL VOC 2007 + 2012, the accuracy of the improved algorithm increased by 1.2% over the original algorithm and 2.2% over the popular YOLOv5. convolutional neural networks; global features; object detection +
Footnote †: journal: Applied  
At present, object detection algorithms are widely used in real-world scenarios such as drone driving, intelligent transportation, and automatic driving. However, despite their application, existing object detection algorithms still have the following problems:  
* The images input to neural networks are often multiscale, so object detectors cannot achieve the desired performance. Even two-stage object detection algorithms have difficulty in extracting finer-grained features;
* The deep convolutional layer integrates the features extracted from the bottom convolutional layer, so the deeper the network is, the richer the global feature information, but more convolutional layers result in the loss of more detailed information;
* Higher-level features correspond to larger receptive fields and can obtain global information about the instance, such as pose. Lower-level features retain better location information and can provide finer details. It is a constant problem in object detection to adopt an effective feature fusion method to fuse the semantic information between different layers. In this paper, to address the above problems in object detection, based on YOLOX [11], a cheaper convolution method replaces a part of the convolution method of the backbone network in the model to obtain image features, which reduces the extracted feature parameters in the model. In order to obtain more fine-grained features, a local feature extraction module is added to the middle layer of the backbone network. Considering the abundance of global information in the deeper layers, global feature modules are added to the deeper convolution layers to reduce the loss of global information during the convolution process. To add weights to the features from different layers and let the model learn the contribution of features from different layers, the idea of \"feature map reuse\" is proposed. This solves the shortcoming of introducing a large number of convolution parameters to adjust the channel dimension when summing up the feature maps from different layers; i.e., the dimension can be improved without adding any parameters. Therefore, the main contributions of this paper are as follows:  
1. Replacing the common 3 \(\times\) 3 convolutional approach in the backbone network CSP-darknet53 to extract features of images with fewer parameters, making the backbone network more efficient in extracting features;
2. Designing local extracted feature modules so that different regions on the feature map receive the attention of different convolution kernels to extract fine-grained features;
3.