Enhancing accuracy and efficiency of small object detection in remote sensing an optimized YOLOv5 approach

In the context of the rapid development and widespread application of remote sensing technology, small object detection has become a prominent research focus. Despite the extensive use of the YOLOv5 network in the field of object detection, its performance in detecting small objects, especially in remote sensing images, remains unsatisfactory. Particularly, detecting and recognizing small objects, such as aircraft, pose greater challenges. The reasons for this include the small size of the targets, low contrast between targets and backgrounds, and the lack of comprehensive publicly available datasets. To address these issues, this study constructed a dataset of remote sensing images containing small aircraft targets, which facilitates the network in capturing fine-grained features and improving detection performance, thus compensating for the shortcomings of existing publicly available datasets. Based on the YOLOv5network model, this study proposed the following optimization measures: (1) To tackle the issue of small target sizes, the model structure was simplified to make the feature extraction network more suitable for small objects and to reduce the number of model parameters. (2) In response to the deficiencies in the original model's fusion method, a bidirectional Feature Pyramid Network (BiFPN) was introduced to enhance multi-level feature fusion capability. (3) To reduce the computational complexity of the model, reasonable anchor boxes were designed to enable the model to accurately focus on crucial information during the detection process. Experimental results demonstrate that the proposed algorithm improves the detection accuracy and speed of small aircraft targets in remote sensing images. On our custom dataset, the method achieves excellent results in terms of precision, computational efficiency, and parameter count.