Research and Explainable Analysis of a Real-Time Passion Fruit Detection Model Based on FSOne-YOLOv7

Abstract

Real-time object detection plays an indispensable role in facilitating the intelligent harvesting process of passion fruit. Accordingly, this paper proposes an FSOne-YOLOv7 model designed to facilitate the real-time detection of passion fruit. The model addresses the challenges arising from the diverse appearance characteristics of passion fruit in complex growth environments. An enhanced version of the YOLOv7 architecture serves as the foundation for the FSOne-YOLOv7 model, with ShuffleOne serving as the novel backbone network and slim-neck operating as the neck network. These architectural modifications significantly enhance the capabilities of feature extraction and fusion, thus leading to improved detection speed. By utilizing the explainable gradient-weighted class activation mapping technique, the output features of FSOne-YOLOv7 exhibit a higher level of concentration and precision in the detection of passion fruit compared to YOLOv7. As a result, the proposed model achieves more accurate, fast, and computationally efficient passion fruit detection. The experimental results demonstrate that FSOne-YOLOv7 outperforms the original YOLOv7, exhibiting a 4.6% increase in precision (P) and a 4.85% increase in mean average precision (mAP). Additionally, it reduces the parameter count by approximately 62.7% and enhances real-time detection speed by 35.7%. When compared to Faster-RCNN and SSD, the proposed model exhibits a 10% and 4.4% increase in mAP, respectively, while achieving approximately 2.6 times and 1.5 times faster real-time detection speeds, respectively. This model proves to be particularly suitable for scenarios characterized by limited memory and computing capabilities where high accuracy is crucial. Moreover, it serves as a valuable technical reference for passion fruit detection applications on mobile or embedded devices and offers insightful guidance for real-time detection research involving similar fruits.

Lightweight Non-Destructive Detection of Diseased Apples Based on Structural Re-Parameterization Technique

“…Its network structure, depicted in Figure 3, comprises three main components: the backbone network (Backbone), the neck network (Neck), and the prediction head (Head). The backbone network primarily extracts feature information, incorporating the C2f module, which merges the C3 module of YOLOv5 and the ELAN of YOLOv7 [25] for enriched gradient flow information. The neck network, responsible for feature information fusion, utilizes a multi-scale feature fusion structure (FPN-APN).…”