How can a pet empty bottle counter's dynamic trajectory tracking and anti-duplicate counting mechanisms prevent missed or double counts when multiple bottles are continuously added?
Publish Time: 2025-09-16
At smart recycling stations or automated vending machines, users often place multiple empty PET bottles into the delivery slots quickly and haphazardly—some bottles flatten, others spin and fall, and some even collapse into piles. Faced with this non-standardized, highly dynamic input method, a pet empty bottle counter relying solely on simple photoelectric switches or static image capture is prone to missed, double, and misidentified counts. This not only affects the accuracy of user points distribution but also undermines public trust in the environmental protection system. Therefore, a truly reliable counter must possess the ability to "understand motion." Through dynamic trajectory tracking and anti-duplicate counting mechanisms, it can create order amidst chaos, ensuring that every bottle is accurately identified and counted only once.The key to achieving this goal lies in the shift from "static identification" to "process perception." The system no longer simply monitors whether a bottle passes a certain point but instead monitors its entire motion path. High-speed vision sensors capture continuous images of the delivery channel at millisecond frame rates. Combined with infrared or laser auxiliary lighting, they ensure clear images under varying lighting conditions. Within each frame, an algorithm extracts the contours, analyzes the morphology, and determines the material of any object entering the field of view, initially selecting objects that match the characteristics of PET bottles. However, the real key lies in the correlation between frames. Using an object tracking algorithm, the system establishes a "motion trajectory" for each identified object, observing its complete path from entry, fall, and exit.When multiple bottles are dropped continuously, they may visually overlap, become obscured, or even temporarily adhere to each other. In these cases, trajectory tracking uses motion vector analysis to separate two connected objects. The system determines whether the two connected objects share the same acceleration, rotational direction, and separation tendency. If it detects gradual separation during their fall, it identifies them as two separate entities and establishes independent trajectories for each. For rapidly falling bottle stacks, the system uses a gravity model to predict the falling trend and uses motion continuity to determine which are independently moving units and which are temporarily touching.Anti-duplicate counting relies on trajectory integrity. The system only considers an object a valid delivery if its trajectory begins at the entry zone, completely traverses the recognition zone, and finally exits the exit. If a bottle stalls, rebounds, or becomes stuck, the system will not count it immediately but instead continuously monitor its subsequent movement. If it is ultimately removed or manually removed, the count is canceled; if it continues its descent and completes its entire journey, it is re-counted. This "closed-loop verification" mechanism effectively prevents miscounts caused by stuck bottles, rebounds, or user removal.In addition, the system incorporates multimodal sensing for cross-verification. A weight sensor provides secondary confirmation when a bottle lands in the collection bin. If the visual count and weight change don't match, the system triggers a re-verification process. Airflow or sound sensors can also assist in determining the timing of bottle passage, enhancing robustness.At the software level, the system incorporates intelligent filtering logic to filter out non-bottle interference objects, such as paper scraps, plastic bags, or metal cans. Through long-term learning, the algorithm continuously optimizes its ability to recognize the motion characteristics of typical interference objects, reducing false triggers. Furthermore, the counting logic incorporates "time window" management, setting a reasonable minimum interval threshold to prevent the accumulation of false signals caused by vibration or light flicker.Ultimately, the significance of this mechanism lies not only in technical accuracy but also in building trust between people and the system. When users receive consistent and accurate feedback regardless of delivery speed or method, they will truly trust the system's fairness and reliability. The pet empty bottle counter is no longer a cold machine; it has become an intelligent partner that can understand human behavior and complex movements. With every precise count, it silently supports the integrity cornerstone of the circular economy.
