Research on Lightweight Improved Algorithm for Indoor Target Detection Based on YOLOv5s

The existing indoor target detection algorithms have many problems, such as complex structure, large amount of calculation and large number of model parameters, which are difficult to be deployed to the indoor robot platform with limited computing capacity to achieve efficient target detection. To solve this problem, an improved YOLOV5s detection algorithm is proposed. In this method, ShuffleNetv2 is introduced as the backbone feature extraction network, and CA attention mechanism is adopted on the basis of the improved backbone network, and GSConv and VOV-GSCSP modules are adopted in the neck network. Finally, the bounding regression loss function EIOU is introduced to accelerate the network convergence. The results show that the improved target detection algorithm reduces the model computation by 68.75%, the number of model parameters by 62.2%, the weight file by 59.7%, and the average accuracy mAP is 0.653. The improved target detection model can ensure the detection accuracy while ensuring the lightweight. © 2024 Chinese Nursing Journals Publishing House Co.,Ltd. All rights reserved.