YOLOv8s-CFB: a lightweight method for real-time detection of apple fruits in complex environments

Abstract

With the development of apple-picking robots, deep learning models have become essential in apple detection. However, current detection models are often disrupted by complex backgrounds, leading to low recognition accuracy and slow speeds in natural environments. To address these issues, this study proposes an improved model, YOLOv8s-CFB, based on YOLOv8s. This model introduces partial convolution (PConv) in the backbone network, enhances the C2f module, and forms a new architecture, CSPPC, to reduce computational complexity and improve speed. Additionally, FocalModulation technology replaces the original SPPF module to enhance the model’s ability to recognize key areas. Finally, the bidirectional feature pyramid (BiFPN) is introduced to adaptively learn the importance of weights at each scale, effectively retaining multi-scale information through a bidirectional context information transmission mechanism, and improving the model’s detection ability for occluded targets. Test results show that the improved YOLOv8 network achieves better detection performance, with an average accuracy of 93.86%, a parameter volume of 8.83 M, and a detection time of 0.7 ms. The improved algorithm achieves high detection accuracy with a small weight file, making it suitable for deployment on mobile devices. Therefore, the improved model can efficiently and accurately detect apples in complex orchard environments in real time.