# An Intelligent Weighted Object Detector for Feature Extraction to Enrich Global Image Information
## 2 Related Work
[PERSON] et al. proposed MGN [28] to uniformly divide an image into multiple strips and vary the number of parts in different local branches to obtain local feature representations with many granularities. The Local Relation Networks [29] propose a local network that can fuse the feature maps of multiple channels that express the same information, improving the representation of the neural network with the same number of channels. Stacked convolutional layers can synthesize the local information obtained from the underlying layers to obtain richer global features. However, the more convolutional layers the feature map goes through, the more significant the loss of global information will be. Non-local neural networks [30] focus on the correlation between each pixel point on the image to obtain global information. In recent years, various transformer-based modelshave emerged due to the global character of transformers. The first one is DETR [31], which successfully integrates a transformer into the object detection model, simplifies the detection process, and outputs the set of prediction results directly in an end-to-end manner. Inspired by MobileNet and transformers, Microsoft proposed a mobile-former for the parallel design of MobileNet and a transformer [32], that is, to establish a bidirectional bridge between MobileNet and a transformer. Thus, convolution and a vision transformer are combined to realize the bidirectional fusion of local and global features. In view of the imbalance between foreground and background and the serious loss of global information in object detection, [PERSON] and [PERSON] proposed FGD [33], which separates foreground and background using focal distillation while instructing students in knowledge distillation by using teachers' spatial and channel attention as pre-training weights, as well as using the proposed global distillation to extract global information from students and teachers separately. Considering the importance of the receiver domain in the feature extraction network, LoFTR [34] borrows the self-attention and mutual-attention layers from a transformer to obtain feature descriptions between images and proposes a new local image feature matching method, which successively performs coarse-grained and fine-grained feature detection as well as matching of image features, making the obtained global receiver domain capable of producing dense feature matching results in regions with small texture information. Sparse R-CNN [35] is a method for sparse detection of image objects without enumerating object locations on the image grid or performing object queries and interactions of global image features. The core ideas are sparse object candidates and sparse feature interaction, and in the head part of the network, the output features and output boxes of the previous head are used as the proposal features and proposal boxes for the next head. **Feature fusion part:** Lower-level features have higher resolution and contain more information about location and details, but have lower semantics and more noise; higher-level features have stronger semantic information, but have lower resolution and poor perception of details. Therefore, the fusion of feature maps of different levels can obtain valuable information. Common practice for the early fusion of features is to perform concat or add operations on the feature map. The concat operation increases the number of channels describing the image itself, while the add operation increases the amount of information per dimensional channel depicting the image itself. In late fusion, in FPN [36], the multi-scale fusion of feature maps is performed in three parts: a bottom-up pathway, a top-down pathway, and lateral connections, thus reducing the parameters of the model.