YOLOv7-GCM: a detection algorithm for creek waste based on improved YOLOv7 model

Abstract

To enhance the cleanliness of creek environments, quadruped robots can be utilized to detect for creek waste. The continuous changes in the water environment significantly reduce the accuracy of image detection when using quadruped robots for image acquisition. In order to improve the accuracy of quadruped robots in waste detection, this article proposed a detection model called YOLOv7-GCM model for creek waste. The model integrated a global attention mechanism (GAM) into the YOLOv7 model, which achieved accurate waste detection in ever-changing backgrounds and underwater conditions. A content-aware reassembly of features (CARAFE) replaced a up-sampling of the YOLOv7 model to achieve more accurate and efficient feature reconstruction. A minimum point distance intersection over union (MPDIOU) loss function replaced the CIOU loss function of the YOLOv7 model to more accurately measure the similarity between target boxes and predictive boxes. After the aforementioned improvements, the YOLOv7-GCM model was obtained. A quadruped robot to patrol the creek and collect images of creek waste. Finally, the YOLOv7-GCM model was trained on the creek waste dataset. The outcomes of the experiment show that the precision rate of the YOLOv7-GCM model has increased by 4.2% and the mean average precision (mAP@0.5) has accumulated by 2.1%. The YOLOv7-GCM model provides a new method for identifying creek waste, which may help promote efficient waste management.