Detecting method of small vehicle targets based on improved SSD

For the task of detecting objects on the ground such as vehicles, it’s difficult to obtain good detection results for the reason of small size and little appearance information of objects and the interference of complex background. Aiming at the problem of accurate localization of small size targets on the ground, a sub-network with feature fusion was proposed from the perspective of target feature extraction. The fusion network introduced important context information, and effectively improved the precision of small target detection.To solve the problem of transformation of sizes and angles, a extensional feature pyramid network was designed as prediction module based on fused feature. Prediction boxes were generated on different scales of extensional feature layers to match the specific objects.Experiments were conducted on the small vehicle target data set VEDAI (vehicle detection in aerial imagery).Results indicate that while the algorithm retains the advantages of detection speed of traditional SSD (single-shot multibox detector), it can significantly improve the accuracy and greatly improve the practicability of the algorithm.