To address the challenges of the absence of shared datasets covering unique targets in unstructured scenes (such as construction sites and mining sites),the difficulty in precise extraction of complex features,and the high computational complexity,this paper creates a dedicated object detection dataset for unstructured scenes.We present a lightweight object detection model named YOLO-PT,which attains high detection accuracy while requiring minimal computational resources.We mitigate the computation of redundant feature information by developing a partial feature calculation(PFC)model.We also incorporate a multi-head self-attention mechanism to enhance the precision of complex feature extraction and design a multi-channel pyramid structure for the gradual fusion of multi-scale features,thereby improving the recognition accuracy of complex objects.Finally,experimental validation is conducted in unstructured scenarios.The results demonstrate that the method proposed achieves the accuracy of 53%with a mere 4.3×10⁶ parameters,outperforming other methods in terms of accuracy,the number of parameters and floating-point operations.