A multiscale remote sensing image strip target detection method without anchor points is proposed to address the issue of the poor detection performance of strip targets in optical remote sensing image target detection. Accordingly, an atrous spatial pyramid pooling feature extraction network that integrates a coordinate attention module and strip pooling module is designed in this study. Consequently, the extraction of strip target feature information is significantly improved. The proposed method was validated through effectiveness experiments on the DIOR dataset, and the optimal dilation rate was selected. Based on this, ablation experiments were conducted to verify the feasibility of the designed framework. Comparative experiments were further conducted considering four classic object detection methods. Compared with the classic anchor-free method CenterNet, the average precision mean of the proposed method improves by 8.63 percentage points and 11.45 percentage points at thresholds of 0.5 and 0.75, respectively. Moreover, compared with the other three methods, the proposed method is competitive. The experimental results show that the proposed method is more accurate in locating strip targets (such as bridges and airports) in optical remote sensing images and has better detection performance.