CR-FOD: foreign object detection based on contrastive analysis of key regions

In the realms of flight safety, agricultural monitoring and medical diagnostics, the detection of foreign objects within images processing technology is of importance. While recent deep learning-based detection methods have enhanced overall performance compared to traditional image techniques, they often encounter challenges in identifying small or novel foreign objects due to data biases. To overcome this limitation, a new approach termed foreign object detection based on contrastive analysis of key regions (CR-FOD) has been introduced. CR-FOD is bifurcated into two phases: Foreign Object Promoter (FOP) and Contrastive Region Object Detection (CROD). During FOP, potential foreign object locations are marked through key point comparison. In the CROD phase, Objects are extracted from the inference image and the corresponding background image based on key-point prompt information by FOP. Finally, the similarity of the objects is compared by CROD to determine the presence of a foreign object at the location. In addition, the efficacy of CR-FOD is confirmed through experimental testing, demonstrating its superior performance in detecting a wide array of foreign objects, particularly those that are minute or previously not encountered during the training datasets.