MCISP-YOLOv5: YOLOv5-based multiscale channel interactive spatially perceptive small object detection algorithm

Two central problems often faced when shooting at high altitudes from UAVs are many small and dense targets and complex background noise interference. In YOLOv5, due to multiple downsampling, the feature representation of small targets becomes weaker and may even be masked in the background. The Feature Pyramid Network (FPN) diminishes the detection accuracy of small targets due to its basic feature concatenation method, which underutilizes multiscale information and introduces extraneous contextual details. To solve the above problems, we propose a simple and effective improved model called multiscale channel interactive spatial perception yolov5 (MCISP-YOLOv5). First, we design a multiscale channel interaction spatial perception(MCISP) module, which recalibrates the channel features in each scale by interacting with information from different scales, facilitates the information flow between the shallow feature geometric information, and the more profound feature semantic information, and uses adaptive spatial learning to realize spatial perception so that the model can focus on the foreground objects better. Second, we replace the traditional up-sampling module with the Content-Aware ReAssembly of Features (CARAFE) operator for feature extraction, which enhances the feature characterization ability after multiple down-sampling, better recovers the detailed information. Finally, we added an additional, shallower depth feature map as the detection layer in YOLOv5. The supplementary feature map enhances the detection efficacy for small objects without adverse effects on the detection capabilities for other sizes of targets. Extensive experiments on the publicly available VisDrone2019 dataset show that the introduced model exhibits substantial enhancements in performance.