The compressive sensing （CS）-based space optical remote-sensing （SORS） imaging system can simultaneously perform sampling and compression by using hardware at the sensing stage. The system must reconstruct the original scene during the ship detection task. The scene reconstruction process of CS is computationally expensive， memory intensive， and time-consuming. This paper proposes an algorithm named compressive sensing and improved you only look once （CS-IM-YOLO） for direct ship detection based on measurements obtained by the imaging system. To simulate the block compression sampling process of the imaging system， the convolution measurement layer with the same stride and convolution kernel size is used to perform the convolution operation on the scene， and the high-dimensional image signal is projected into the low-dimensional space to obtain the full-image CS measurements. After obtaining the measurements of the scene， the proposed ship detection network extracts the coordinates of the ship from the measurements. The squeeze-and-excitation Network （SENet） module is imported into the backbone network， and the improved backbone network is used to extract the ship feature information using the measurements. The feature pyramid network is used to enhance feature extraction while fusing the feature information of the shallow， middle， and deep layers， and then to complete predicting the ship's coordinates. CS-IM-YOLO especially connects the convolutional measurement layer and the CS based ship detection network for end-to-end training； this considerably simplifies the preprocessing process. We present an evaluation of the performance of the algorithm by using the HRSC2016 dataset. The experimental results show that the precision of CS-IM-YOLO for detection of ships via CS measurements in SORS scenes is 91.60%， the recall is 87.59%， the F1 value is 0.90， and the AP value is 94.13%. This demonstrates that the algorithm can perform accurate ship detection using the CS measurements of SORS scenes.