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To address the problem of low detection accuracy caused by high background complexity, diverse target sizes, and an abundance of small objects in remote sensing images, a remote sensing image object detection algorithm based on improved YOLOv5 is proposed. This algorithm employs a ConvNeXt network with Transformer-style architecture as the backbone network to overcome the limitations of CNN structures and capture more global information. The SimAM attention mechanism is introduced to infer 3D attention weights of the feature maps without increasing the network parameters, thereby enhancing the stability and anti-interference capability of the network. Additionally, a concentrated feature pyramid (CFP) with a global explicit focus adjustment scheme is utilized to capture long-range global dependencies and local key region information in remote sensing images. The proposed algorithm is evaluated on the RSOD dataset through ablation experiments, and the results demonstrate a significant improvement in the average accuracy of remote sensing image object detection.