Small Object Detection Algorithm Based on Improved YOLOv8s
To address the challenges of small object size,complex backgrounds,insufficient feature extraction capabilities,and significant issues of false and miss detections in object detection tasks,an improved small object detection algorithm called Improved-v8s is proposed,which is based on YOLOv8s architecture.Firstly,the feature extraction and fusion networks are redesigned,the detection layer architecture is optimized,and the fusion of shadow and deep-level information is enhanced,which improves the sensing and acquisition capabilities of small objects.Secondly,within the feature extraction network,Partial Convolution(PConv)and Efficient Multi-scale Attention(EMA)mechanisms are used to construct a novel feature fusion module named F_C2f_EMA,which effectively reduces network parameters and computational complexity while maximizing the preservation of small object features through channel reshaping and dimension grouping.To better match the scale of small objects,the kernel size of the SPPCSPC pooling operation is optimized and adjusted,and the Simple-parameter-free Attention Module(SimAM)is also introduced to enhance small object feature extraction in complex backgrounds.Furthermore,a lightweight upsampling module CARAFE is incorporated in the Neck module,which facilitates feature recombination and expansion to preserve more detailed information.Then a Global Attention Mechanism(GAM)is introduced to model the contextual information of small objects through global context association,fully leveraging the contextual information for small object detection.By leveraging GSConv and Effective Squeeze-Excitation(EffectiveSE),a novel G_E_C2f module is designed to further reduce parameters,effectively reducing the false and miss detection rates in the model.Finally,the WIoU loss function is used to address the challenges of target imbalance and scale differences,accelerating the model convergence while improving the regression accuracy.Experimental results demonstrate that the Improved-v8s algorithm achieves Precision,Recall,and mean Average Precision(mAP)of 58.5％,46.0％,and 48.7％,respectively,on the VisDrone2019 dataset,which are improved by 8％,8.5％,and 9.8％respectively as compared with the original YOLOv8s network.The model's small object detection capabilities are significantly enhanced.Generalization experiments on the WiderPerson and SSDD datasets also validate that the algorithm outperforms other classical algorithms.
small object detectionYOLOv8sGAMCARAFEloss function
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