DSPs （digital signal processors） using VLIW （very long instruction word） architecture are widely used in high real-time application scenarios， such as image processing and computer vision. One of the important algorithms in these application areas is the highly parallel multi-directional Sobel algorithm. Implementing and optimizing this algorithm for VLIW DSPs is of great significance. In this paper， we propose a method of optimizing convolutional computation based on VLIW data rearrangement Im2col （image to column） plus matrix multiplication GEMM （general matrix multiplication）， and use DMA （direct memory access） double buffer mechanism to realize the parallelism of data transmission and kernel computation， which reduces the time overhead of waiting for data transmission and the time overhead of kernel computation. The time overhead of waiting for data transmission is reduced， and the multi-directional Sobel algorithm is implemented and optimized on FT-Matrix DSP using this method. The experimental results show that the optimized algorithm achieves 4.96~8.76 times speedup compared with the algorithm in OpenCV image library， and 3.26~6.60 times improvement compared with the TMS320C6678 processor. These results show that the DSP with VLIW architecture has significant advantages in intensive data processing， and the image detection algorithm implemented and optimized on VLIW DSP has a broad application prospect.