Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paper proposes an improved You Only Look Once version 7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the shape features of the target, deformable convolutional networks (DCN) is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed. Second, to enhance the extraction of important features and suppress useless features, this paper proposes a new convolutional block attention module_efficient channel attention (CBAM_E) for embedding the neck network to improve the model's ability to extract features from complex scenes. Finally, to reduce the influence of angle factor and bounding box regression accuracy, this paper proposes a new alpha-SCYLLA intersection over union (alpha SIoU) instead of the complete intersection over union (CIoU), which improves the regression accuracy while increasing the convergence speed. Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes, with an increase of 2.92% in the precision rate, 4.14% in the recall rate, 0.0356 in the weighted harmonic mean, 3.60% in the mAP@0.5 value, and a reduction in the number of parameters and complexity. Compared with the mainstream algorithm, the improved algorithm has higher detection accuracy, faster convergence speed, and better actual recognition effect, indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.