To overcome the limitation that the existing dropped object detection methods cannot identify the “unknown category”, this study proposed a dropped object detection architecture based on feature similarity learning. Firstly, the reference image and the query image to be detected were obtained during the dropping process. The appearance features were extracted through a weight-shared siamese convolutional network. Then, Euclidean distance was used to measure dissimilarities between features of reference image and query image. Finally, dropped objects were detected by selecting the pixel from the distance map whose distance value was larger than the fixed threshold. In order to improve its robustness to noise such as illumination change, this paper proposed a novel attention mask unit. And the semantic discriminativeness of the mask was improved through constructing the long-span contextual information and strong supervised learning method. This finally guides the feature response to focus on the appearance changes caused by the dropped objects while ignore the disturbance caused by noise, and solves the problem of feature entanglement between noise and the dropped objects. In order to verify the effectiveness of the method, this study collected data in a real highway scene and built a standard dataset. The results show that the attention mask unit effectively improves the semantic discriminative of features and greatly improves the accuracy of dropped object detection, which achieves F₁ an improvement of 6.4 percentage points. Meanwhile, the algorithm reaches in 30 FPS, which can be performed in real-time. The long-span context information constructed by the feature sequence state transition method is more conducive to attention mask focusing on the projectile feature information, and has stronger anti-noise interference ability. The attention mask contour obtained by strongly supervised learning is more accurate and the model accuracy is higher.