Connecting rods of automobile engines are the main transmission parts, the quality of which directly affects the transmission performance of engines and ultimately influences the safety of vehicles. To realize the simultaneous detection of geometric parameters, bending, and twisting of connecting rods, the multi-parameter quality detection based on machine vision is proposed for them. Specifically, a vision system for multi-parameter quality detection is constructed for the connecting rods. The image preprocessing based on multi-threshold analysis and homomorphic filtering is studied to remove the shadow and enhance the contrast of the engine connecting rod images. The geometric features such as lines and circles are detected in these images based on the sub-pixel level analysis and Hough transform. Then, the least square method is used to fit the parameters of these geometric features with a further analysis of quality parameters. In this way, the multi-parameter quality detection for the connecting rods of automobile engines is realized. An application example has proved the effectiveness of the proposed method.