# The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
## 1 Introduction
The Swin Transformer [15] classification model, based on a Transformer architecture with visual attention, is particularly well-suited for handling large-sized images and long-range information interactions. It introduces a hierarchical, window-based attention mechanism to process information at different scales, thereby enhancing the model's ability to understand images. The YOLO algorithm [16], based on deep learning, has the feature of single-stage object detection, which allows for the fast and accurate detection of different targets, making it highly suitable for fruit recognition and evaluation tasks. The improvements to the YOLO algorithm are primarily reflected in speed, accuracy, robustness, and flexibility. Through optimizations in network architecture and computational efficiency, YOLO has further enhanced detection speed. The introduction of new feature extraction networks, anchor box mechanisms, and multi-scale feature fusion techniques has significantly improved accuracy. Enhancements in robustness allow the model to better handle occlusion, complex backgrounds, and varying lighting conditions during detection tasks. Furthermore, YOLO offers multiple configuration options, making the model more flexible, allowing users to select the most suitable version based on their specific requirements and computational resources. These improvements have enabled the YOLO algorithm to exhibit greater adaptability and reliability in practical applications. Numerous studies have shown that enhancing the model's feature extraction and fusion capabilities can effectively improve the accuracy and stability of fruit recognition. The introduction of attention mechanisms allows the model to selectively focus on important, relevant features while ignoring less important information. [PERSON] et al. [17] proposed SENet, a channel attention mechanism consisting of three parts: compression, excitation, and channel weight updating. First, global average pooling is used to compress the feature map of each channel. Then, a fully connected layer learns the correlation between the channels and uses an activation function (sigmoid) to generate the weight for each feature map group, enhancing the important features and suppressing the less important ones. Finally, the channel weight vector is updated by multiplying it with the original input. Considering that SE-Net performs mapping at a single scale, [PERSON] et al. [18] proposed SK-Net, which operates at multiple scales and enables the network to autonomously learn to select and fuse feature map information from different receptive fields. While SE-Net only considers the contribution of feature map channels, it neglects the fact that the spatial location of objects in an image also plays a crucial role in object detection. [PERSON] et al. [19] proposed CBAM, which combines channel attention and spatial attention mechanisms in series, effectively improving the network's feature extraction and representation capabilities. However, although CBAM integrates channel attention and spatial attention mechanisms, it does not consider cross-dimensional interaction. [PERSON] et al. [20] established interactions from three branches: the width and height of the image, the dimension and width, and the dimension and height. The proposed CTAM (Cross-Transformer Attention Module) demonstrated outstanding performance. To avoid the loss of spatial information caused by 2D global pooling in SE-Net, [PERSON] et al. [21] proposed CA, which decomposes channel attention into two parallel 1D feature encodings and embeds spatial position information into the channel attention. This approach allows the model to capture more information while avoiding significant overhead. Due to the large side effects of dimensionality reduction and the inefficiency of unnecessary channel interactions, [PERSON] et al. [22] proposed ECA-Net, a local cross-channel interaction strategy that does not reduce dimensionality, maintaining performance while reducing model complexity.