An algorithm for external quality detection of potato incorporating spatial pyramid pooling and up-sampling operators

In response to the current issues of missed detections, false alarms, low detection accuracy, and incomplete detection categories in potato quality assessment, this paper proposes an enhanced YOLOv5s-based object detection model. The model categorizes external potato features into four standard classes. Firstly, this paper introduces the SE (Squeeze and Excitation) attention mechanism, enabling the model to adaptively adjust feature weights across different channels, emphasizing features crucial for quality assessment. Secondly, incorporate the SPPCSPC (Spatial Pyramid Pooling Cross Stage Partial Channel) convolutional neural network structure, transforming various-sized feature maps into fixed-length feature vectors. Additionally, employ the up-sampling operator CARAFE (Content-Aware Reassembly of Features) to enhance the performance of the feature pyramid network. Experimental results demonstrate that the improved YOLOv5s model exhibits a 4.5% increase in detection accuracy and a 2.1% improvement in average precision.