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Pedestrian detection is an important part of the perception system of autonomous vehicles. Foggy and low-light conditions are quite challenging for pedestrian detection, and several models have been proposed to increase the robustness of detections under such challenging conditions. Checking if such a model performs well is largely evaluated by manually inspecting the results of object detection. We propose a monitoring technique that uses Timed Quality Temporal Logic (TQTL) to do differential testing: we first check when an object detector (such as vanilla YOLO) fails to accurately detect pedestrians using a suitable TQTL formula on a sequence of images. We then apply a model specialized to adverse weather conditions to perform object detection on the same image sequence. We use Image-Adaptive YOLO (IA-YOLO) for this purpose. We then check if the new model satisfies the previously failing specifications. Our method shows the feasibility of using such a differential testing approach to measure the improvement in quality of detections when specialized models are used for object detection. 

We present eMOP, a tool for incremental runtime verification (RV) of test executions during software evolution. We previously used RV to find hundreds of bugs in open-source projects by monitoring passing tests against formal specifications of Java APIs. We also proposed evolution-aware techniques to reduce RV’s runtime overhead and human time to inspect specification violations. eMOP brings these benefits to developers in a tool that seamlessly integrates with the Maven build system. We describe eMOP’s design, implementation, and usage. We evaluate eMOP on 676 versions of 21 projects, including those from our earlier prototypes' evaluation. eMOP is up to 8.4x faster and shows up to 31.3x fewer violations, compared to running RV from scratch after each code change. eMOP also does not miss new violations in our evaluation, and it is open-sourced at https://github.com/SoftEngResearch/emop.