Ms. Haitong Sun Profile

Haitong Sun

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Proceedings Article | 14 November 2023 Paper

A new FHDW based on improved YOLO-E

Yufeng Li, Jinfeng Li, Haitong Sun

Proceedings Volume 12934, 129340E (2023) https://doi.org/10.1117/12.3008033

KEYWORDS: Object detection, Facial recognition systems, Performance modeling, Feature extraction, Emotion, Deep learning

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This article proposes a Facial Expression Hierarchical Detection Network (FHDN), use a convolutional neural network based on multiple branches, about facial expression detection. To further improve feature extraction performance, the method adds an ESSAM module as an attention mechanism. The ESSAM module can adaptively adjust the weight of each feature map, thus improving the performance of the model in feature extraction and facial expression recognition tasks. The method was experimentally evaluated on a self-made dataset, and the results showed that the detection accuracy rate of this model was 81.40%, which is an improvement of 5% and 0.7% compared to YOLOV5 and YOLOV8, respectively. When compared to conventional deep learning techniques, this approach extracts picture characteristics more quickly and accurately without the need for labor-intensive manual labor.

Proceedings Article | 11 October 2023 Paper

Where the TinyPerson escapes: SOLEnet small object location estimates from coarse to fine

Xueying Zhao, Jinfeng Li, Haitong Sun, Yufeng Li

Proceedings Volume 12800, 1280042 (2023) https://doi.org/10.1117/12.3003845

KEYWORDS: Small targets, Object detection, Target detection

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Existing object detection methods primarily focus on detecting large targets in images, with limited research conducted on small targets. Additionally, there are challenges with low detection accuracy and difficulty in meeting real-time requirements. Based on this, an improved RetinaNet is proposed. First, the use of high-resolution feature maps can better capture the features of small targets. Then a weight factor is added to solve the unbalanced distribution of training samples caused by using high-resolution feature maps. Finally, adding a novel query subnet by two main steps: first estimate the coarse location of the small target in the high-level feature map, then the exact location is calculated in the low-level feature map. Experiments show that this query subnet saves the computational cost of low-level feature maps, and greatly improved detection speed. The performance of the model is evaluated on a new benchmark called TinyPerson, mAP up to 36.18, FPS up to 13.98. Compared to the unimproved RetinaNet, the accuracy and speed are improved by 0.9 and 1.28 respectively.

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