# Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning
## 3 Object Detection  
[PERSON] [7] proposed SSDLiteX, a modified version of SSDLite that enhances small object detection by prioritizing higher-resolution feature maps and increasing the number of layers in the base CNN, resulting in a 1.5 percent point improvement in average precision (AP) for small objects in the MS COCO dataset.  
[PERSON] et al. [8] proposed a drowsiness detection method focusing on the eye region using Mediapipe for extraction, and evaluated three deep learning networks--InceptionV3, VGG16, and ResNet50V2--on the NITYMED dataset. The results indicate high accuracy across all networks.  
[PERSON] et al. [9] introduced YOLOv5s-Z, a lightweight tennis ball detection algorithm. It leverages a G-Backbone and G-Neck network to reduce parameters and computations, integrates convolutional coordinate attention for location information enhancement, employs a modified concat module for efficient feature fusion, introduces EIOU Loss and Focal-EIOU Loss to handle aspect ratio imbalance, and incorporates Meta-ACON activation for improved accuracy.  
[PERSON] et al. [10] proposed Auto-T-YOLO, a detection network consisting of three stages: preattention, attention, and prediction. The experimental results verify the practicality, validity, and robustness of the proposed model.  
[PERSON] et al. [11] proposed a kitchen standard dress detection method, leveraging the YOLOv5s embedded model to swiftly and accurately identify whether a chef is wearing a hat and a mask. By constructing a comprehensive kitchen scene dataset and introducing images of chefs wearing masks and hats, the method mitigates the reliability issue associated with single-object detection. Additionally, the integration of YOLOv5 and DeepStream SDK on Jetson Xavier NX facilitates the real-time detection and early warning of non-standard dress in kitchen environments.  
[PERSON] et al. [12] proposed a novel insulator defect detection algorithm based on YOLOv5 to enhance accuracy and speed. It addresses challenges such as background interference and small fault areas by constructing a lightweight backbone network, increasing the detection layer for small targets, and designing a receptive field module.  
[PERSON] et al. [13] introduced a multi-channel MSER (Maximally Stable Extreme Regions) method and an enhanced Feature Pyramid Network (FPN) for street sign text detection in complex backgrounds. The multi-channel MSER method effectively reduces background and light interference by leveraging color information. The enhanced FPN incorporates a Feature Pyramid Route Enhancement (FPRE) module and a High-Level Feature Enhancement (HLFE) module to exploit low-level and high-level semantic information, improving text localization and detection across various shapes, sizes, and orientations.  
[PERSON] et al. [14] introduced a compound convolutional neural network (CNN) architecture for face mask detection. The proposed architecture combines two computer vision tasks: object localization to detect faces using RetinaFace, followed by image classification using ResNet-18 architecture to categorize face mask usage as correct, incorrect, or absent. Furthermore, the authors have released the dataset used for model training and the computer vision pipeline to the public, optimized for deployment on embedded systems.