The complex distribution, mutual occlusion, and scale difference greatly increase the difficulty of cotton detection in the wild. To reduce the omission ratio and raise the detection accuracy of cotton, a dual-path feature extraction (DPFE) cotton detection algorithm is proposed. It consists of a DPFE convolutional neural network, a multi-path feature fusion module, and a multi-scale prediction module. First, the algorithm uses the Darknet network as the main path for feature extraction. At the same time, the double downsampling feature map of the main path is enhanced by a proposed feature enhancement module—spatial pyramid convolution. Then a four-layer convolutional neural structure is designed as the auxiliary path for feature extraction. Finally, multiple feature information is incorporated to locate and recognize cotton with a higher accuracy. In addition, we collected and labeled a cotton dataset with 168 high-resolution images, including 4922 cotton instances for research. The experimental results demonstrate that the DPFE algorithm increases the average detection precision by 9.55% and the recall rate by 13.69%, compared with the traditional algorithm.