Lightweight object detection algorithm for robots with improved YOLOv5.

Robot object detection is important for the realisation of robot intelligence. Currently, deep learning-based object detection algorithms are used for robotic object detection. However, it faces some challenges in practical applications, such as the fact that robots frequently use resource-constrained devices, resulting in detection algorithms with long computation times and undesired detection rates. In order to address these concerns, this paper proposes a lightweight object detection algorithm for robots with an improved YOLOv5. To reduce the amount of processing required for feature extraction and increase the speed of detection, the C3Ghost and GhostConv modules have been introduced into the YOLOv5 backbone. The DWConv module was used in conjunction with the C3Ghost module in the YOLOv5 neck network to further reduce the number of model parameters and maintain accuracy. The CA (Coordinated Attention) module is also introduced to improve the extraction of features from detected objects and suppress irrelevant features, thus improving the algorithm's detection accuracy. To verify the performance of the method, we tested it with a self-built dataset (4561 robot images in total) and the PascalVOC dataset respectively. The results show that compared with the YOLOv5s on the self-built dataset, the algorithm has a 54% decrease in FLOPs and a 52.53% decrease in the number of model parameters without a decrease in mAP (0.5). The effectiveness and superiority of the algorithm is demonstrated through case studies and comparisons. • Lightweight C3Ghost and GhostConv modules are introduced in the YOLOv5 backbone network to achieve model compression and maintain detection accuracy and speed. • C3Ghost and DWConv modules are introduced in YOLOv5 neck network to further reduce model parameters and improve the speed of feature fusion. • CA (Coordinated attention) module is also introduced to enhance the extraction of relevant features and suppress irrelevant features to improve the detection accuracy of the algorithm. • To demonstrate the algorithm's ability to solve real-world problems, we produced a dataset for the object detection task in the RoboMasterAI challenge hosted by DJI and experimentally verified that our proposed object detection algorithm is effective. [ABSTRACT FROM AUTHOR]