<prompt> Hlalosa algorithm bakeng sa mosebetsi o fanoeng.Algorithm bakeng sa ho lemoha leseli la sephethephethe  <response>: Instruction in English: Describe an algorithm for a given task. . Response in English: Traffic light detection is the process of detecting traffic lights and determining their status to aid autonomous vehicles or driver assistance systems. Heres a high-level algorithm for Traffic light detection:1. Image acquisition: Acquire or capture an image of the front view of the vehicle using a camera mounted on the car.2. Preprocessing: Perform image preprocessing to enhance the image quality as well as remove background noise. This step might include image resizing, contrast enhancement, and image smoothing.3. Feature extraction: Extract relevant features from the image that are essential for traffic light detection. This step can be performed using color segmentation, edge detection, or blob detection.4. Object recognition: In this step, the algorithm identifies objects in the image using detection techniques like a Haar cascade or Histogram of Oriented Gradients HOG. It is important that the algorithm is trained on many images of traffic lights to correctly identify the objects within the image.5. State detection: Once the traffic light is recognized, the state of the light i.e., red, yellow, or green is determined. The state can be found by examining the RGB values of the pixels within the traffic light and comparing them to predetermined threshold values.6. Post-processing: Perform post-processing such as determining the position and state of all traffic signals in the image and conveying the information to the cars control system to take the appropriate action.7. Repeat Steps: Keep repeating the steps in real-time to ensure continuous and accurate traffic light detection. . Response in Sesotho: Ho lemoha mabone a sephethe-phethe ke mokhoa oa ho bona mabone a sephethe-phethe le ho fumana boemo ba ona ho thusa likoloi tse ikemetseng kapa lisebelisoa tsa thuso ea bakhanni. Mona ke algorithm ea boemo bo holimo bakeng sa ho lemoha mabone a Sephethephethe: 1. Fumana setšoantšo: Fumana kapa u nke senepe sa ponahalo e ka pele ea koloi u sebelisa khamera e kentsoeng koloing. 2. Preprocessing: Etsa setšoantšo esale pele ho ntlafatsa boleng ba setšoantšo hammoho le ho tlosa lerata le ka morao. Mohato ona o ka kenyelletsa ho fetola boholo ba setšoantšo, ho ntlafatsa phapano, le ho theola setšoantšo. 3. Tlhahiso ea likarolo: Ntša likarolo tse amehang setšoantšong tse bohlokoa bakeng sa ho lemoha khanya ea sephethephethe. Mohato ona o ka etsoa ho sebelisoa karohano ea mebala, ho lemoha moeli, kapa ho lemoha blob. 4. Tlhokomelo ea ntho: Mohato ona, algorithm e khetholla lintho tse setšoantšong ho sebelisa mekhoa ea ho lemoha joaloka Haar cascade kapa Histogram of Oriented Gradients HOG. Ke habohlokoa hore algorithm e koetlisitsoe litšoantšong tse ngata tsa mabone a sephethephethe ho khetholla ka nepo lintho tse ka hara setšoantšo. 5. Ho lemoha ha naha: Hang ha lebone la sephethephethe le lemohuoa, boemo ba lebone ke hore, bofubelu, bosehla, kapa bo botala boa lekanyetsoa. Naha e ka fumanoa ka ho hlahloba litekanyetso tsa RGB tsa pixels ka hara leseli la sephethephethe le ho li bapisa le litekanyetso tse reriloeng esale pele. 6. Post-processing: Ho etsa post-processing joalo ka ho fumana boemo le boemo ba matšoao ohle a sephethephethe setšoantšong le ho fetisetsa boitsebiso ho tsamaiso ea koloi ho nka khato e nepahetseng. 7. Pheta Mehato: Tsoela pele ho pheta-pheta mehato ka nako ea sebele ho netefatsa hore ho na le leseli le tsoelang pele le le nepahetseng la sephethephethe. 