Accurate polyp segmentation is crucial for the early detection of colorectal cancer. However, existing polyp detection methods sometimes ignore multi-directional features and the drastic scale changes of concealed targets. To address these challenges, we design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. The Orthogonal Direction Convolutional (ODC) block can extract multi-directional features using transposed rectangular convolution kernels through forming sets of orthogonal feature vector basis, which solves the issue of random feature direction changes. Additionally, the Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions, enhancing the segmentation accuracy for polyps of varying sizes. Extraction with Re-attention (ERA) module is used to re-combine effective features, and Shallow Reverse Attention (SRA) mechanism is used to enhance polyp edge with low level information. A large number of experiments conducted on public datasets have demonstrated that the performance of this model is superior to state-of-the-art methods.