Improved Lightweight YOLOv3 model for Target Detection Algorithm

Abstract

When detecting small objects in interior situations, the classic object detection algorithm performs poorly in terms of real-time detection task and high precision detection task. This paper suggests an optimized tiny-YOLOv3-Shufflenetv2 light-weight model based on indoor scenes. The scheme adopts the fusion light-weight network which combines ShuffleNetv2 and YOLOv3, it reduces the complexity of the model to meet the lightweight requirements while ensuring good detection results for deployment to mobile robots. Also in this paper, an indoor small target object dataset, indoor-2022, is created to improve and optimize the model for the data images. YOLOv3, YOLOv3-Shufflenetv2, and tiny-YOLOv3-Shufflenetv2 are trained and tested on the indoor-2022 small target dataset in the Pytorch framework. The experimental findings indicate that in the indoor-2022 dataset. Compared with the single YOLOv3 model for object detection tasks, the fusion improved model used in this article improves the recognition ability of small objects in indoor images, With a 10-fold reduction in model size and a 4-fold increase in detection speed, only results in 1.6% reduction in the mean accuracy (mAP), and the comparison experiments with the current stage of traditional target detection algorithms validate the proposed tiny-YOLOv3-Shufflenetv2 model is verified to be superior and feasible. The optimized model in this article reduces mannequin parameters and model size while additionally ensuring the accuracy and velocity of inspection, and meets the requirements for deployment on indoor mobile robots.