Computer Vision Engineer简历模板

• Systematically studied theoretical knowledge related to computer vision, including courses such as image processing and pattern recognition, with excellent grades, GPA 3.8 (out of 4.0) • Participated in scientific research projects in the school's computer vision laboratory, accumulating preliminary practical experience

• Responsible for the development of the computer vision module in the company's intelligent security system, led the optimization of pedestrian detection algorithms, increased the detection accuracy rate from 85% to 92%, deployed in actual projects, covering more than 50 communities, effectively reducing the false alarm rate • Led a 3 - person team to develop the vehicle recognition function, from data collection (establishing a dataset containing more than 100,000 vehicle images), model training (using deep learning frameworks, optimizing hyperparameters) to online deployment, increased the vehicle recognition speed by 30%, and the recognition accuracy rate reached above 95% • Closely cooperated with the product team, proposed new function plans according to market demands, such as abnormal behavior detection, promoting the improvement of the product's competitiveness in the industry

• Led the computer vision part of the company's medical image analysis project, developed lesion detection algorithms for lung CT images. Through data augmentation (operations such as rotation, scaling, expanded the dataset from 5,000 cases to 20,000 cases), model improvement (using a new convolutional neural network architecture), increased the lesion detection sensitivity from 78% to 90%, and this result was trialed in 3 top - tier hospitals, receiving praise from doctors • Responsible for building the company's computer vision algorithm platform, integrating common object detection, image segmentation and other algorithms, supporting quick call and customized development, increasing the team's development efficiency by more than 40% • Cooperated with university scientific research teams to carry out leading - edge technology research, such as Transformer - based medical image analysis, and published 1 related paper (EI - indexed)

• Project Background: Developed the product image search function for a large e - commerce platform to enhance the user shopping experience • Project Responsibilities: Responsible for the design of image feature extraction algorithms, constructed a dataset containing more than 5 million product images, and optimized image retrieval using deep hashing algorithms • Project Achievements: Increased the recall rate of image search from 70% to 85%, doubled the search speed, and increased the frequency of users using this function by 40% after launch

• Project Background: Developed a road scene recognition system for an autonomous driving company • Project Responsibilities: Participated in the development of the road sign recognition module, collected and sorted more than 200,000 road sign image data, annotated and preprocessed them, and optimized the recognition model using transfer learning • Project Achievements: Achieved a road sign recognition accuracy rate of 98%, and in actual road tests, the system's adaptability to complex road scenes (rainy days, nights, etc.) increased by 30%