EB-YOLO:An efficient and lightweight blood cell detector based on the YOLO algorithm

Abstract

Blood cell detection is an important part of medical diagnosis. Object detection is trending for blood cell analysis, with research focusing on high-precision neural network models. However, these models have complex architectures and high computational costs. They cannot achieve rapid detection on low-end devices. Although lightweight models can greatly enhance the detection speed and achieve the real-time detection on low-end devices, their accuracy is poor in complex tasks. The development of efficient and highly accurate blood cell detectors for environments with limited computational resources is of great practical value. This study proposes an Efficient Blood Cell Detector based on YOLO (EB-YOLO) for blood cell detection. The model uses ShuffleNet as the backbone network for feature extraction to reduce the number of parameters and computational load. It incorporates the Convolutional Block Attention Module (CBAM) to enhance feature representation. In the neck network, Adaptive Spatial Feature Fusion (ASFF) is used for feature integration to improve multi-scale target feature extraction. Depth-wise separable convolution replaces standard convolution to reduce parameters while maintaining performance. Experimental results on the BCCD dataset show that the proposed model achieves 92.1 % mAP@50 %, the computational complexity is only 0.9 GFLOPs, and the number of parameters is 0.289M. The comparison results of the inference speed on Raspberry PI 5 show that the detection speed of the model is better than the classic YOLO algorithm model. The proposed method successfully balances lightweight design and high accuracy, which shows promise for deployment on low-end embedded systems.