Garbage object detection based on improved YOLOv7

Garbage target detection plays a crucial role in environmental protection and urban management. With the continuous development of computer vision technology, deep learning methods have become one of the main tools for garbage target detection. YOLOv7 is an advanced object detection model known for its high speed and accuracy, but it still faces challenges in garbage target detection. In order to enhance the performance of YOLOv7[1] in garbage target detection, this study proposes an approach based on improved YOLOv7.Firstly, we introduce the GAMAttention[2] attention module to enhance the model's ability to detect small targets. Secondly, we incorporate the Decoupled Head[3] to improve the model's robustness to targets in complex backgrounds. Additionally, we optimize the model's loss function to further enhance detection accuracy. The proposed garbage target detection method based on improved YOLOv7 has made significant progress in improving detection performance, providing more effective tools and technical support for environmental protection and urban management. This approach is expected to be widely promoted in practical applications, addressing challenges and issues in the field of garbage target detection.