# An Intelligent Weighted Object Detector for Feature Extraction to Enrich Global Image Information
## 2 Related Work  
**Feature extraction by convolution:** In image feature extraction, the traditional method is generally divided into three steps: pre-processing, feature extraction, and feature process ing. Finally, machine learning and other methods are utilized to classify the features and other operations. The purpose of image pre-processing is mainly to eliminate interference factors and highlight feature information; the main method used is image normalization. Traditional feature extraction mainly uses feature extractors to extract features from images, such as the SIFT algorithm, which is commonly used in the field of image matching. It can maintain invariance for rotation, scale scaling, and changes in brightness, and is highly resistant to occlusion, but it is computationally intensive. The HOG algorithm for pedestrian detection, although it reduces the dimensionality of the representation data needed for images, has poor real-time performance [15]. Since the rise of deep learning, convolutional neural networks have gradually replaced manual feature extraction methods. Generative adversarial networks generate similar images by learning the distribution of original image data and consist of a generator and a discriminator [16], while convolutional neural networks learn data features and extract beneficial information from them. A properly designed convolutional network can bring qualitative changes to model detection performance. DenseNet [17] achieves the ultimate utilization of features to achieve better network results with fewer parameters. The lightweight network MobileNet [18; 19] is a lightweight deep neural network proposed by Google for embedded devices such as cell phones. Its core idea is deep separable convolution, which greatly reduces the parameters of convolution in the model. In the ShuffleNet series [20; 21], pointwise group convolution and random channel shuffle are used to achieve a balance between model detection speed and accuracy, making it possible to deploy this network with low latency on the mobile side. EfficientV1 [22] explores the relationship between input resolution and network depth and width, and achieves a balance of the three dimensions according to the scaling provisions, while EfficientV2 [23] introduces the Fused-NBConv module and a progressive learning strategy based on EfficientV1 while employing a non-uniform scaling strategy to adjust the model size. ResNeSt [24] uses multiple convolutional kernels in the same layer to extract features separately, while introducing a soft attention mechanism to achieve weight distribution among feature channels, and finally proposes a modular split-attention block. In GhostNet [25], there are many redundant feature maps in the high-dimensional feature maps generated deep in the network, so an inexpensive convolution operation is designed to generate feature maps of equal channels and build a lightweight network. A sparse feature reactivation method is proposed in CondenseNet V2 [26], where each convolutional layer can selectively reuse the most important features from the previous layer and update the features in the previous layer to achieve the utility of the updated feature layer. In the case of ReXNet [27], it was argued that excessive reduction in the size of the feature map may lead to information loss and cause problems of degraded model performance, so [PERSON] et al. conducted an in-depth study of the rank of the feature matrix generated by a variety of random networks and designed a more accurate network structure that moderates the side effects of representative bottlenecks. **Global information of the feature:** Global features describe the overall attributes of an image, including color features, texture features, and shape features. Local features are features extracted from local regions of an image, including edges, corner points, lines, curves, and areas of specific attributes. In recent years, local image features have been widely applied in face recognition, 3D reconstruction, and target recognition and tracking.