Small object detection based on enhanced multiscale contextual features

In aerial photography and remote sensing imagery, among other domains, a proliferation of small objects is frequently encountered, which, due to their homogenous and sparse textural characteristics, pose formidable challenges to existing object detection algorithms, leading to suboptimal performance in their detection. To tackle these issues, this paper presents an augmented model, MSCF-YOLO, grounded in YOLOv8, with a focus on enhancing multi-scale features and integrating object contextual information. This is achieved through the incorporation of Poly Kernel Inception Blocks and Channel Prior Convolutional Attention mechanisms, thereby enriching the model's feature representation capacity. Additionally, the introduction of the Powerful-IOU guided small object anchoring strategy facilitates regression towards more favorable paths, thereby augmenting the model's learning proficiency pertaining to small objects. Empirical assessments conducted on the test set of the VisDrone2019-DET dataset revealed that the proposed MSCF-YOLO model achieves a mean Average Precision (mAP) of 42.2%, surpassing its baseline counterpart, conclusively affirming the efficacy of the proposed enhancements.