MIoP-NMS: Perfecting crops target detection and counting in dense occlusion from high-resolution UAV imagery

Abstract

When performing dense crops target detection and counting tasks in high-resolution images of large scene, several algorithms such as the standard Non-Maximum Suppression (NMS) and its improved CIoU-NMS and soft-NMS are commonly used to improve the de-duplication performance of the prediction frame in objects detection, which has a high mean average precision (mAP) but the error boundaries may be large. In this research, an improved NMS-based max intersection over portion (MIoP-NMS) algorithm is proposed to address this problem and implemented in the YOLOv4 network framework for single-stage target detection. Using banana tree target detection statistics with different sparsity levels as an experimental case, the mAP obtained by applying the method in the paper reaches about 85%, with a mean counting error of about 1.3%. In comparison with several commonly NMS methods, their mAP values are comparable. However, compared to the 10%∼25% mean count error when using these commonly used methods, the mean count error of the method proposed in the paper is reduced by about 8.7%∼23.7%. The method in the paper estimates the number of banana trees in dense occluded banana forests with about 98.7% accuracy, which is simple and effective, and has significant advantages for accurate detection and counting of dense crops targets in high-resolution images in large scenes.

Keywords

• MIoP-NMS
• Deep Learning
• Detection counting
• Large scene high resolution
• Dense banana tree
• Precision agriculture