Lightweight peach detection using partial convolution and improved Non-maximum suppression

Abstract

The automation of fruit detection within the agricultural sector is an essential component for enhancing efficiency and ensuring high-quality produce. Among various crops, peaches present a unique challenge due to their soft texture, susceptibility to damage, and the common occurrence of overlapping fruits. These characteristics not only complicate manual inspection but also pose significant hurdles for automated detection systems. In this paper, we propose a new lightweight model for scenarios with a large number of overlapping peaches and low-quality samples in the training dataset. We refer to the proposed model as the Lightweight Peach Detector (LP-Det). Specifically, we proposed Inshape-IoU-Soft-Non-maximum suppression (ISIS-NMS) algorithm to refine the model’s precision and accuracy in detecting overlapping peaches, and proposed Partial Convolution Batch-normalization SiLU (PCBS) module to diminish the model’s size and expedite inference. Moreover, we introduced Wise-Intersection over Union v3 (WIoUv3) to mitigate the influence of low-quality samples on model training and enhance localization accuracy. Our method was assessed using a peach dataset acquired from a peach orchard, demonstrating superior performance compared with several state-of-the-art (SOTA) object detection models.