YOLO v7-CS: A YOLO v7-Based Model for Lightweight Bayberry Target Detection Count

Abstract

In order to estimate bayberry yield, a lightweight bayberry target detection count model, YOLOv7-CS, based on YOLOv7, was proposed to address the issues of slow detection and recognition speed, as well as low recognition rate, of high-density bayberry targets under complex backgrounds. In this study, 8990 bayberry images were used for experiments. The training set, validation set, and test set were randomly recreated in a ratio of 8:1:1. The new network was developed with SPD-Conv detection head modules to extract features at various scales, to better capture small and indistinct bayberry targets. To improve accuracy and achieve a lightweight design, a CNxP module that replaces the backbone’s ELAN structure is proposed. We propose a global attention mechanism (GAM) in the intermediate layers of the network, to enhance cross-dimensional interactions, and a new pyramid pooling module called SPPFCSPC, to extend the field of perception and improve boundary detection accuracy. Finally, we combine the Wise-IoU function to enhance the network’s ability to identify overlapping and occluded objects. Compared with the SSD, Faster-RCNN, DSSD, and YOLOv7X target detection algorithms, YOLOv7-CS increases mAP 0.5 by 35.52%, 56.74%, 12.36%, and 7.05%. Compared with basic YOLOv7, mAP 0.5 increased from 5.43% to 90.21%, while mAP 0.95 increased from 13.2% to 54.67%. This parameter is reduced by 17.3 m. Ablation experiments further show that the designed module improves the accuracy of bayberry detection, reduces parameter counts, and makes bayberry image detection more accurate and effective.

A lightweight waxberry fruit detection model based on YOLOv5

“…mAP reached 93.7%, but the network parameter was 6.9 M, which was not much different from YOLOv5. Li et al [26] proposed an improved YOLOv7, adding an SPD module and Global Attention Mechanism (GAM) for detecting Waxberry fruits, with a final map of 90.21%, and the number of parameters was 124.5 M and also encountered the problem of too large model parameters. Wu et al [27] used the YOLOv7 network and oil camellia images for multiple data enhancement, and the final mAP, Precision, Recall, F1 score, and average detection time for each image were 96.03%, 94.76%, 95.54%, 95.15%, and 0.025 s, respectively.…”

“…Li et al. [26] proposed an improved YOLOv7, adding an SPD module and Global Attention Mechanism (GAM) for detecting Waxberry fruits, with a final map of 90.21%, and the number of parameters was 124.5 M and also encountered the problem of too large model parameters. Wu et al.…”

Research on improved YOLOv8n based potato seedling detection in UAV remote sensing images

“…However, traditional manual monitoring methods are costly, inefficient, inaccurate, and often lack representativeness, which impedes the timely and effective implementation of replanting strategies ( Lu et al., 2023 ). The advent of drones, characterized by their agility, compact size, and cost-effectiveness, has increasingly attracted the attention of researchers ( Saifizi et al., 2019 ; Li S. et al., 2023 ). Utilizing drones in conjunction with deep learning for the automatic detection of crop seedlings presents a simple yet effective method that significantly reduces labor costs and facilitates automation.…”

Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head

“…By combining the Ghost module [19] with CBS and C3 modules, the model size and computation are significantly reduced, and the CBAM [20] attention mechanism is introduced to enhance the model's ability to extract strawberry features. Li Shuo et al [21] in response to the slow recognition speed of high-density bayberries under complex backgrounds, designed a lightweight bayberry counting model YOLOv7-CS based on YOLOv7 [22]. They proposed the CNxP module to replace the E-Elan module in the backbone, achieving model lightweight while improving the model's detection accuracy and positioning ability.…”